Plastic waste presents a growing environmental challenge due to its persistence and increasing accumulation, making efficient and accurate classification of plastic types–including PET, HDPE, PVC, LDPE, PP, PS, and others– essential for real-time automated recycling systems. The inherent heterogeneity of plastic types presents significant classification challenges that a single network cannot adequately address. In this work, we propose a class-aware multi-expert knowledge distillation framework to deploy high-capacity models on resource-constrained edge devices. Accordingly, specialized models have been developed, each serving as an ’expert’ for specific plastic classes. By leveraging these ensemble classifiers within a multi-expert teacher knowledge distillation framework, we have introduced a compact student model, further optimized into an even tinier variant suitable for real-time embedded deployment. The performance of such models is heavily contingent upon the quality of the training dataset. This challenge is particularly pronounced in plastic waste due to the high morphological variability of materials and their susceptibility to deformation or alterations in appearance caused by aging and contamination. Consequently, existing public datasets often lack the diversity required for robust model generalization. To address this limitation, we have consolidated multiple datasets into a unified framework, providing a valuable benchmark for automated plastic waste classification. The generalizability of the proposed framework has been demonstrated across multiple student and teacher models using both homogeneous and heterogeneous model backbone architectures. Experimental results demonstrate that the proposed distillation approach enables the tiny student to achieve high accuracy and robustness while significantly reducing model size (by 8.3) and inference time.

Waste management is becoming an intriguing problem all over the world. waste’s volume is becoming more and more huge due to the urbanization and industrialization. This issue impacts all components of the environment, including air, water, and land. This alarming situation needs some solutions which should be rapid, urgent and innovative. For this reason, several solutions have been proposed to reduce the huge amount of waste globally. In fact, recycling may be an efficient tool, especially when the sorting process is done automatically. As this is one of the main goals of the circular economy, many researchers have focused on developing automatic solutions to address environmental problems. Recently, with rapid advancement of Artificial Intelligence (AI), different solutions were developed in order to improve accuracy and save time. Many investigations on AI techniques have been made in order to include machine learning, deep learning and ensemble approaches. This paper reviews AI based approaches for waste classification while emphasizing their real world scope. In addition, it highlights the most popular dataset used in order to validate the efficiency of the developed solutions. The main goal of this paper is to provide an overview of the existing techniques of AI helping researches to built a more accurate solution for a cleaner environment.

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. This paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. The data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. The system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge.

Plastic waste is becoming nowadays a huge environmental problem. Circular economy seems to be the best choice for preserving the environment, by reinserting plastic waste into the industrial process and use. A key step for that is the plastic waste segregation from other waste. This paper proposes a new plastic waste segregation approach based on two modalities: spectroscopy and optical images. The research handles an optical camera for waste image acquisition, while the ground truth for plastic type identification, with respect to the seven primary plastic types: PET, HDPE, LDPE, PP, PS, PVC, and Other, is established through spectroscopic analysis. Various deep learning models were trained and evaluated on a dataset of publicly available and custom-collected images, aiming to achieve human-level accuracy in distinguishing between plastic types. The proposed methodology is based on a set of steps, where the model is first built implicitly on a segregation of different wastes, and then applied via transfer learning for separating the seven plastic types. The system performances demonstrate the feasibility and effectiveness of this combined approach for accurate and efficient plastic waste sorting. The proposed system has been implemented on Raspberry Pi 4 Model B+ as the central processing unit, integrating computer vision and deep learning techniques for efficient and accurate sorting.

Visual examination of defects in the industry is a tedious task requiring great attention and time to scrutinize several parts. That’s why implementing an automated system for defect detection should be placed to mitigate such disruption in the production line. Such systems leverage several image processing techniques in order to identify and extract defect areas. Unfortunately, the similarity between defect types makes defect classification very ambiguous. To cope with such a limitation, possibility theory offers a modeling space where uncertainty and ambiguity can be conveniently handled. In this context, the Dubois and Prade fromalism is applied on texture feature’s regions containing defaults basing on Local Binary Pattern (LBP). Accordingly, a possibility mapping of the different defects, which yields a more reliable decision making within the possibility/necessity paradigm. This approach’s effectiveness is demonstrated through a comparative study against other state-of-the-art techniques. The proposed method outperforms others by achieving a mean Precision mP of 81.6% on the NEU-DET database.

Nowadays, the demand for embedded image processing, especially for computer vision applications is growing. Digital image processing is seeing intensive use in real time applications for several areas, such as the industrial environment, military equipment, automobile, entertainment, and medical instruments. Mathematical Morphological theory, offers a wide variety of tools, applied efficiently in most image processing steps at different scales, like image enhancement, segmentation, and classification. Therefore, such features yield a high potential of reconfiguration in image processing blocks. Morphological opening and closing operators are used as primary blocks in diverse image processing applications such as image cleaning and contrast enhancement. Moreover, using appropriate shapes and scales of structuring elements(SE), provides more flexibility and can significantly improve the enhancement performance. Nevertheless, such operators can be computationally intensive due to the increased complexity and the significant memory requirement. Also, the use of high image resolution and reconfigurable size and shape of structuring elements may deepen this limitation significantly. In this paper, we consider hardware acceleration on FPGA as a promising solution to raise the above mentioned-challenges. Both intra-level and inter-level parallelism techniques based on the reuse of optimized dilation and erosion blocs are exploited to accelerate computation and save memory consumption. Unlike most existing works, this paper proposes a highly flexible architecture for opening and closing-based morphological filters. The architecture supports re-configurable shapes and sizes of structuring elements while keeping a high ability to meet real-time processing requirements and low power consumption. An improvement in terms of frame rates up to 8% with more than 2000 Fps for moderate size of structuring elements and high image resolution is achieved.

Breast cancer early diagnosis is a major concern for reducing deadly cases. Automation of microcalcification detection is becoming increasingly important given their tiny scale. At this step, a high false negative rate is observed, leading to high ambiguity. In this context, conventional approaches are unable to handle such ambiguity. Possibility theory offers a powerful paradigm enabling to handle a high uncertainty level. Therefore, in this research,new possibilistic modeling strategy for microcalcification detection is proposed. The developed system is based on k-means clustering followed by a fusion of the corresponding possibility distributions, for decision making. For enhancing classification's accuracy, two aspects may be taken into consideration: scattering within classes and the class discrimination power. A high inter-class variance may be regarded as an evidence of good discrimination. Nevertheless, in case of existing highly scattered classes, miss-classifications of samples at the class margins are still encountered, even at high inter-class variance. Clustering solves this problem by redefining classes in a more compact way. Under this paradigm, the clustering optimization is performed using a criteria based on the Area Under Curve(AUC), where the confidence degree level is based on the consistency principle of Dubois and Prade. The above strategy has been applied for the detection of microcalcifications, based on a set of pyramidal and multi-resolution based features. Validation on the Digital Database for Screening Mammography (DDSM) public dataset has been undertaken. The proposed system, which gives 99.4 % detection accuracy, can be used to assist medical practitioners.

An understanding of the image starts with sensing pertinent information, and subsequently recognize domain objects, based on a prior conceptualization. Thus, suitable modeling of the image content is essential to make use of the dependency between patterns in a particular domain. Through a computer interpretable model that results in a knowledge-based model, we can optimize the leverage of knowledge in image interpretation of a certain domain. In this paper, we focus on a systemic knowledge modeling intended for surface defect classification. Accordingly, we have exploited the image spatial information for building surface defect domain ontology. A set of statistical texture features has been extracted. A systemic approach of conceptualisation has been proposed, based on a decision tree classification, looking at filling the gap between low and medium level knowledge on the one hand and high level knowledge, which is defect detect categories on the other hand. Accordingly, the proposed ontology has been modeled with OWL and SWRL for reasoning and rule inference. The information, extracted from the grayscale image and its significance for deducing the surface flaws, is formalized to establish surface defect ontology. Validation of the proposed approach has been done on an industrial radio-graphs dataset NEU-DET. Compared to the state-of-the-art, our method yields on the same dataset a challenging performance of 85.87 % in term of mean average precision (mAP).

Nowadays, the COVID-19 pandemic imposes the use of a contactless biometric system to prevent the spread of contagious diseases efficiently. Hand veins are contactless and independent of the body’s appearance. However, researches on age and gender estimation by hand veins are very limited. They focused only on age group discrimination and not the exact age. Estimating the age and gender by veins features is a challenging task since hand vein images are poor in quality and subject to variation in illumination. In this paper, a finger vein gender and age recognition system based on Pyramidal histograms of oriented gradient (PHOG) is presented. PHOG can better describe both the local shape and the spatial distribution of the veins as the image is divided into sub-regions at different resolutions for which the HOG descriptor is applied. Experimental validation on finger vein databases MMCBNU 6000 and UTFVP demonstrates the effectiveness of extracted features in gender classification and age estimation including ages from 16 to 72 years with an uncertainty of one year. The middle finger of the left hand provides the best results for both age and gender classification (F-measure 100%) for MMCBNU 6000 database, whereas for UTFVP database, F-measure is about 98,62% for age estimation and 99,47% for gender classification. A comparison study with recent approaches is carried on, showing an improvement of F-measure by 5.76% for age estimation and 1.38% for gender classification.

Dendritic cells can be seen as a mirror of our immune system. Based on their in virto analysis, biological experts are now able to study the impact of food contaminants on the human immune system. Accordingly, a visual characterization of dendritic cell morphology can provide an indirect estimation of the toxicity. In this paper, we propose an automatic classification of dendritic cells that could serve as a second non-subjective opinion for pathologists. The proposed approach is built on pre-processing steps for segmentation and cell detection in microscopic images. Then, a set of features such as shape descriptors are extracted for cell characterization. At this step, three cell classes are distinctively identified by experts. Nevertheless, a high ambiguity is revealed between cell classes. Possibility theory can offer a realistic framework for making reliable decisions under high ambiguity. It exploits a human natural concept of the implicit use of probability distribution for deciding on the possibility of some assertions in some contexts where a cognitive conflict is observed while interfering existing related postulates, leading to high ambiguity. Based on the consistency concept of Dubois and Prade, a transformation of the probability into a possibility distribution is undertaken. Under possibility paradigm, a further feature selection in the possibility space using the Shapely index. Compared to state-of-the art methods the proposed approach yielded on a real dataset of nearly 630 samples an improvement in terms of the mean precision rate, the Recall rate, and the F1-measure.

Abstract Mammography continues to play a central part in the early breast masses diagnosis and has raised several challenges in breast cancer detection. Nevertheless, it remains still difficult to detect abnormalities in a dense breast and some benign lesions may have similar appearance with masses. This study proposes a novel Computer‐Aided Diagnosis (CADx) allowing benign and malignant mass classification. Accordingly, relevant textural‐shape descriptors are proposed, which is the aggregation of a new Local Binary based feature, namely the Monogenic Gray Level and Local Difference (MGLLD), where both texture characteristics and breast tissue density information, and the Zernike moments are incorporated. A heuristic algorithm is then devised for optimizing the proposed features with respect to inter‐class and intra‐class distribution, by a convenient ponderation. Then, a set of classifiers are applied for opting to the best decision making solution. The Deep CNN yields the best accuracy, with an Area Under Curve (AUC) of 0.98 on Curated Breast Imaging Subset of DDSM (CBIS‐DDSM). In addition, the authors are based on a subjective approval of the extracted Region Of Interest (ROI) by experts in radiology. The proposed scheme proves its effectiveness especially on some challenging breast cancer cases, corresponding to higher breast tissue density. In medical image processing, these results open new perspectives with respect to the use of the deep learning solutions, where the small sample‐number is known to be a limitation (because pathologies cannot be intentionally provoked).

Melanoma is the most serious type of skin cancer. Early detection of melanoma can increase survival rates. Recently, the emergence of deep learning approaches for medical image analysis has improved the development of computer-aided diagnosis systems that can help the expert to make a better decision about patient health. While dermoscopic image requires a specialized device for image acquisition, optical skin images acquired with a standard camera is an attractive modality for melanoma early diagnosis, yielding acceptable lower detection rates, reported in the state of the art, at a reasonable cost. In this paper, we consider this modality for improving the detection accuracy of melanoma. However, there are no large available datasets of optical lesion images as for dermatoscopic modality; for training deep networks. Thus, accordingly, we propose here a transfer learning paradigm that helps to overcome such limitations. A new CNN architecture and a set of deep learning networks have been trained. The best detection rates are obtained by the convolutional neural network, yielding a detection rate of 97%.

This paper presents a reconfigurable 2D parallel architecture designed to implement efficiently two fundamental gray scale morphological operations: dilation and erosion. The architecture is expected to be used as a hardware core integrated in real time application. Moreover, the proposed architecture allows processing data on high resolution images with reconfigurable size and arbitrary shape of structuring elements. The main advantage of the proposed architecture is its low latency, higher throughput and higher processing frame rate. Additionally, the architecture processes data on stream which avoids the need of any buffering at input level. The architecture is successfully implemented and prototyped on Virtex-5 field programmable gate array. Implementation results show that the architecture can achieve high frame rate: for example, for a 1024x768 image and 11x11 structuring element, we reach a frame rate of 341 Fps.

The region of interest (ROI) extraction is important in hand vein recognition system. The main challenges for accurate extraction of the vein region are to overcome variability in hand size, lighting conditions, orientation, appearance, noisy background, and non‐uniform grey levels in foreground region. Here, we propose a new dynamic hand vein ROI extraction, preserving the whole vein area. A hand segmentation process robust to the mentioned challenges, contributing to an accurate definition of hand edge delimitations is proposed. Our approach is validated on both dorsal vein Bosphorus database and palm vein Vera database. Our proposed method accuracy is ∼98% for Bosphorus database and 90% for Vera database. To illustrate the efficiency of the proposed ROI extraction, we insert it as a first block in a hand vein recognition system. Then, a comparison study at system level with recent approaches is carried on, showing an improvement of the whole system area under the curve by a rate of 12% and 2% for Bosphorus and Vera databases, respectively. The speed performances demonstrate a mean run time of 0.73 s for Bosphorus database and 1.2 s for Vera database, proving that the proposed method can be conveniently used on a real‐time application.

In this paper, we focus on analysis and diagnosis of breast masses inspired by expert concepts and rules. Accordingly, a Bag of Words is built based on the ontology of breast cancer diagnosis, accurately described in the Breast Imaging Reporting and Data System. To fill the gap between low level knowledge and expert concepts, a semantic annotation is developed using a machine learning tool. Then, breast masses are classified into benign or malignant according to expert rules implicitly modeled with a set of classifiers (KNN, ANN, SVM and Decision Tree). This semantic context of analysis offers a frame where we can include external factors and other meta-knowledge such as patient risk factors as well as exploiting more than one modality. Based on MRI and DECEDM modalities, our developed system leads a recognition rate of 99.7% with Decision Tree where an improvement of 24.7 % is obtained owing to semantic analysis.

In this paper we propose a novel process of a fully automatic breast tissue segmentation which is independent from expert calibration and contrast. The proposed algorithm is composed by two major steps. The first step consists in the detection of breast boundaries. It is based on image content analysis and Moore-Neighbour tracing algorithm. As a processing step, Otsu thresholding and neighbors algorithm are applied. Then, the external area of breast is removed to get an approximated breast region. The second preprocessing step is the delineation of the chest wall which is considered as the lowest cost path linking three key points; These points are located automatically at the breast. They are respectively, the left and right boundary points and the middle upper point placed at the sternum region using statistical method. For the minimum cost path search problem, we resolve it through Dijkstra algorithm. Evaluation results reveal the robustness of our process face to different breast densities, complex forms and challenging cases. In fact, the mean overlap between manual segmentation and automatic segmentation through our method is 96.5%. A comparative study shows that our proposed process is competitive and faster than existing methods. The segmentation of 120 slices with our method is achieved at least in 20.57±5.2s.

This paper describes ontology-based semantic analysis of lesion images. We first present our ontology focusing on its main concepts, as well as the semantic annotation. Accordingly, the Bag-of-Words (BoW), modeling these concepts in skin lesion diagnosis, is inspired from experts in dermatology. These BoWs are modeled from the lesion images. Firstly, we extract low-level features describing the lesion shape, color and texture. Secondly, the BoWs are generated from these features using a machine learning classifier (SVM). An important step in semantic analysis is to define rules relating the different concepts. In our case, these rules are inspired from the score of the ABCD rule for decision making. Experimental results on a public database of 206 lesion images demonstrate that ontology offers a more efficient frame of analysis, where semantic relations between concepts can handle more knowledge of experts, and can be more appropriate for lesion severity classification with a good accuracy. Comparing to the previous works, our approach yields good sensitivity (97.4%) and accuracy (76.9%).

International audience

Melanoma is the most dangerous form of skin cancer. It can be developed from pigmented cells of the skin and can grow and spread swiftly to other organs (metastasis). An early diagnosis increases the chance of cure. In the past three decades, the increase in the incidence of melanoma has given rise to more accurate methods of analysis. Feature extraction is a critical step in melanoma decision support systems. Early dermatoscopic rules (ABCD rule, 7-point checklist, Menzies method and CASH algorithm), used by experts are generally low level features. In this paper, we consider several dermatoscopic rules for automatic detection of melanoma in order to generate new high level features allowing semantic analysis. Such extracted features are based on shape characterization and color and texture features. A neural network classifier is used for decision making. Experimental results indicate that semantic analysis is a useful method for discrimination of melanocytic skin tumors with good accuracy. The proposed method yields a good sensitivity of 92% and a specificity of 95% on a database of 206 skin lesion images. A comparative study with recent previous works illustrates that our approach outperforms in terms of accuracy and specificity.

This paper focuses on breast masses analysis from two different modalities: Magnetic Resonance Imaging (MRI) and Dual-Energy Contrast Enhanced Digital Mammography (DECEDM). After the segmentation step, a set of texture and shape features are extracted from both MRI and DECEDM. Then textural and morphological information extracted from the two modalities are combined in order to improve breast cancer detection. Achieved results show that features combination extracted from two different breast images modalities can give a better characterization of breast cancer with a CCR of 96%.

Unimodal biometric recognition systems have random performances which can be efficient for some contexts and not for others. Fusing multiple modalities improves recognition performance and reduces some limitations of biometric systems based on a single modality, such as intentional fraud or universality problem (inability to acquire data from certain persons for some modes). Furthermore, in real-world applications, biometric data imperfections affect the performance of the recognition algorithms which may be caused by several factors such as the poor image quality, noise and adverse illumination and/or contrast. To handle data imperfections or redundancy and cover data variability, possibilistic modeling is a powerful tool. In this paper, we propose a robust multimodal biometric recognition system integrating fingerprint and palmprint based on possibilistic modeling approach. The proposed approach relies on possibility theory concepts for modeling biometric features. Accordingly, a set of relevant biometric features extracted from image samples is statistically analyzed and represented by a possibility distribution. The biometric templates from the palmprint and fingerprint are used for decision making by applying a score level data fusion process. Validation of the proposed approach is done on the public palmprint image database, CASIA (Chinese Academy of Sciences Institution of Automation) and the public fingerprint database, FVC (Fingerprint Verification Competition). As performance metric, we adopt the area under the ROC curve. Each both modalities give an AUC (0,8694) and (0,9531) respectively for palmprint and fingerprint when used alone. A typique multimodal system using both modalities gives an AUC of (0,9531). The proposed multimodal system has an AUC very close to unity (0,9997).

In this paper, a self-constructing neuro-fuzzy (SCNF) classifier optimized by swarm intelligence technique is proposed for breast cancer diagnosis. The first step in the design is the definition of the fuzzy network structure. Accordingly, a rule generation approach with self-constructing property is proposed. Based on similarity measures, the given input-output patterns are organized into clusters. Then, membership functions are generated roughly to form a fuzzy rule base. To achieve accurate learning, particle swarm optimization (PSO) algorithm is used to adjust consequent and antecedent parameters of the obtained rules. Accordingly, a weighted function is constructed to design the objective function of the PSO, which takes into account the specificity, the sensitivity and the total classification accuracy of the proposed SCNF classifier. The proposed SCNF classifier is evaluated on the widely used Wisconsin breast cancer dataset (WBCD) for breast cancer diagnosis. Experimental results confirm that the proposed model is able to detect breast cancer with a classification accuracy of more than 99%. A comparative study has been elaborated confirming the best performance of the proposed classifier.

Augmented reality is an interesting field which use live video imagery to add virtual data to the real world. In this paper, we firstly present a review of recent applications of augmented reality. One of the recent application of the augmented reality is the use of video projection to provide a more comfortable interaction with display devices without any hardware equipment. Thus, we secondly present a review of AR camera-projector based system and its main still open challenges. Visual understanding and image processing present a fundamental part which constitutes the easiest and most natural tool for interacting with a normal user in video projection system. One of the most important challenges is related to image interpretation and user fingertip tracking. In this context, we review the existing methods for hand segmentation, fingertip detection and contact surface detection. Finally, we consider a case study of virtual keyboard for user interface and smart environment. In this study, we propose a fingertip tracking system. We accordingly propose a hand segmentation approach based on new automated version of k-means. Experimental validation on real acquisitions is presented to illustrate the feasibility of the proposed system.

The biometric systems face variability, incompleteness and insufficiency in data, which affects the performance of the recognition system. In iris recognition systems, several conditions cause different types of degradations on iris data such as the poor quality of the acquired pictures, the iris region which can be partially occluded due to light spots, or by lenses, eyeglasses, hair or eyelids, and adverse illuminations or contrasts. All of these limitations are open problems in the iris recognition and affect the performance of iris localization, iris feature extraction or decision making process, and appear as imperfections in the extracted signature. This paper addresses the use of the uncertainty theory for modeling iris system imperfections. Several comparative experiments were conducted on three subsets, namely CASIA.Ver4: synthetic, thousand and interval iris databases. Experimental results show that our proposed system, based on the possibility theory, improves the iris recognition system in terms ROC, AUC, FAR, FRR and PIN, compared to other iris identification systems.

For a safe navigation of visually impaired people, traversable area segmentation seems well important for preventing collision or falling down. In this respect, reducing the system cost entails applying low cost low resolution monocular cameras, leading to poor quality images. Adding potential navigation associated camera vibration, the system proves to be liable to some data imperfections. Such image affecting imperfections refer well to the necessity of applying the possibility theory as an appropriate means for reducing information ambiguity. In this regard, a new traversable area segmentation based on possibility modeling theory approach is being developed in this work. Noteworthy and for satisfactory adaptation of our model to image condition variability, a crucial starting point is imposed, namely considering reference area placed in the bottom of the image, assumed to be a traversable area. Actually, the proposed approach involves three major steps, namely: a color feature extraction step, a possibility distribution generation step, based on Dubois and Prade principle, as well as a data fusion step where a conjunctive operator is achieved for generating a distinctive unique possibility map. To note, the proposed approach is validated on a publicly as well as a developed databases. A mean accuracy rate of 98% is obtained with respect to both databases.

In this paper, we propose a new architecture of Neural-Fuzzy Network (NFN) devoted to function approximation tasks. NFN with on chip learning offers the possibility of reconfiguration and the generality of the solution since it can approximate any input-output function through parameters update. Back-propagation learning algorithm constitutes an appropriate method that can make an efficient approximation of NFN parameters. In this context, the main idea is to implement the proposed NFN based on the back-propagation algorithm using Field Programmable Gate Arrays (FPGA). However, the complexity of such system, presents a drawback for hardware implementation. Therefore, we make use of pulse mode since it can support this problem thanks to its higher density of integration. To verify the proposed design performance, we consider image denoising function approximation as illustration example. Experimental results reveal the performance and efficiency of the proposed NFN versus other conventional filtering techniques. Synthesis results on a FPGA platform are presented and discussed.

This paper present a novel architecture for image segmentation. The design is based on the fuzzy c-means algorithm based gaussian function in pulse mode for reducing the large storage requirement. The proposed algorithm is tested in mammogram image segmentation approximately with 0.92 of segmentation index. The pulse mode stochastic computing technique is implemented with a simple bloc avoiding the use of conventional multipliers for sake of compactness. The whole modified FCM is implemented on a Virtex II PRO FPGA platform and synthesis results are presented.

Microcalcifications are tiny deposits of calcium located in breast tissue. They appeared as very small highlighted regions in comparaison with their surrounding tissue. The difference of contrast between microcalcifications and the normal tissue depend on the breast density: The more the breast is dense, the less is the contrast. In this context, we propose to enhance microcalcifications details for each type of breast density using for methods. As we know that the BIRADS/ACR 4 contains dense breast, That's why we have proposed to make the Non Linear Stratching (NLS)automatic by applying an improved Tsallis entropy. The proposed mammography enhancement approach is evaluated on the Digital Database for Screening Mammography (DDSM) database.

In this paper, we propose an implementation of a Neuro-Fuzzy System (NFS) with on chip learning for achieving different image processing tasks such as filtering, edge detection, etc. The complexity of this kind of implementation makes the pulse mode an important approach to achieve our goal thanks to its higher density of integration. As validation example, we propose here the edge detection process to be approximated by this system. The proposed system has proven a good approximation ability with a reduced neuron number and learning time cost. Moreover, the efficiency of our proposed system versus conventional edge detection operators is demonstrated. For different error criteria, our design shows the lowest values. The designed system is implemented on a field-programmable gate array (FPGA) platform. Synthesis results prove that the implemented NFS provides the best compromise between compactness, speed and accuracy compared to previous works from literature.