Efficient and accurate detection of cell nuclei is an important step toward automatic analysis in histopathology. In this work, we present an automatic technique based on generalized Laplacian of Gaussian (gLoG) filter for nuclei detection in digitized histological images. The proposed technique first generates a bank of gLoG kernels with different scales and orientations and then performs convolution between directional gLoG kernels and the candidate image to obtain a set of response maps. The local maxima of response maps are detected and clustered into different groups by mean-shift algorithm based on their geometrical closeness. The point which has the maximum response in each group is finally selected as the nucleus seed. Experimental results on two datasets show that the proposed technique provides a superior performance in nuclei detection compared to existing techniques.

With the development of high‐speed, high‐resolution whole slide histology digital scanners, glass slides of tissue specimen can now be digitised at high magnification to create the whole slide image. Quantitative image analysis tools are then desirable to help the pathologist for their routine examination. Epidermis area is a very important observation area for the cancer diagnosis. Therefore, in order to build up a computer‐aided diagnosis system, segmentation of the epidermis area is often the very first and crucial step. An improved computer‐aided epidermis segmentation technique for the whole slide skin histopathological image is proposed in this study. The proposed technique first obtains an initial segmentation result with the help of global thresholding and shape analysis. A template matching method, with adaptive template intensity value, is then applied. Finally, a threshold is calculated based on the probability density function of the response value image. Experimental results show that the proposed technique overcomes the limitation of the existing technique and provides superior performance, with sensitivity of 95.68%, specificity of 99.41% and precision of 93.13%. The performance of the proposed technique is satisfactory for future clinical use.

Wireless capsule endoscopy (WCE) can directly take digital images in the gastrointestinal tract of a patient. It has opened a new chapter in small intestine examination. However, a major problem associated with this technology is that too many images need to be manually examined by clinicians. Currently, there is no standard for capsule endoscopy image interpretation and classification. Most state-of-the-art CAD methods often suffer from poor performance, high computational cost, or multiple empirical thresholds. In this paper, a new method for rapid bleeding detection in the WCE video is proposed. We group pixels through superpixel segmentation to reduce the computational complexity while maintaining high diagnostic accuracy. Feature of each superpixel is extracted using the red ratio in RGB space and fed into support vector machine for classification. Also, the influence of edge pixels has been removed in this paper. Comparative experiments show that our algorithm is superior to the existing methods in terms of sensitivity, specificity, and accuracy.

The count of mitotic cells is a critical factor in most cancer grading systems. Extracting the mitotic cell from the histopathological image is a very challenging task. In this paper, we propose an efficient technique for detecting and segmenting the mitotic cells in the high-resolution multispectral image. The proposed technique consists of three main modules: discriminative image generation, mitotic cell candidate detection and segmentation, and mitotic cell candidate classification. In the first module, a discriminative image is obtained by linear discriminant analysis using ten different spectral band images. A set of mitotic cell candidate regions is then detected and segmented by the Bayesian modeling and local-region threshold method. In the third module, a 226 dimension feature is extracted from the mitotic cell candidates and their surrounding regions. An imbalanced classification framework is then applied to perform the classification for the mitotic cell candidates in order to detect the real mitotic cells. The proposed technique has been evaluated on a publicly available dataset of 35 × 10 multispectral images, in which 224 mitotic cells are manually labeled by experts. The proposed technique is able to provide superior performance compared to the existing technique, 81.5% sensitivity rate and 33.9% precision rate in terms of detection performance, and 89.3% sensitivity rate and 87.5% precision rate in terms of segmentation performance.

In this paper, we propose an efficient method for segmenting cell nuclei in the skin histopathological images. The proposed technique consists of four modules. First, it separates the nuclei regions from the background with an adaptive threshold technique. Next, an elliptical descriptor is used to detect the isolated nuclei with elliptical shapes. This descriptor classifies the nuclei regions based on two ellipticity parameters. Nuclei clumps and nuclei with irregular shapes are then localized by an improved seed detection technique based on voting in the eroded nuclei regions. Finally, undivided nuclei regions are segmented by a marked watershed algorithm. Experimental results on 114 different image patches indicate that the proposed technique provides a superior performance in nuclei detection and segmentation.

Detection of singular points (SPs) in fingerprint images is an important task in fingerprint recognition. In this paper, we propose a novel technique for SPs detection using orientation field regularization and the Poincaré Index (PI) technique. The squared orientation field is first extracted from a fingerprint image. In order to distinguish the local orientation patterns of genuine SPs from that of spurious SPs, a novel technique based on the Discrete Hodge Helmholtz Decomposition (DHHD) is proposed to reconstruct a regular orientation field of the fingerprint. Based on the regular orientation field, the PI technique is then applied to extract the SPs. Experimental results on the public fingerprint database FVC2002 show that, the proposed technique is rather accurate and robust in identifying SPs.

This letter considers the binary threshold system (TS) based detector for a general binary testing problem. First, the optimal binary TS that maximizes the area under the ROC curve (AUC), where ROC stands for the receiver operating characteristic, is derived. Then the noise-enhanced effect is investigated. The optimal noise that can achieve the maximum AUC is derived and shown to be deterministic. An example is shown to help justify the derived results.

In the diagnosis of skin melanoma by analyzing histopathological images, the detection of the melanocytes in the epidermis area is an important step. However, the detection of melanocytes in the epidermis area is dicult because other keratinocytes that are very similar to the melanocytes are also present. This paper proposes a novel computer-aided technique for segmentation of the melanocytes in the skin histopathological images. In order to reduce the local intensity variant, a mean-shift algorithm is applied for the initial segmentation of the image. A local region recursive segmentation algorithm is then proposed to filter out the candidate nuclei regions based on the domain prior knowledge. To distinguish the melanocytes from other keratinocytes in the epidermis area, a novel descriptor, named local double ellipse descriptor (LDED), is proposed to measure the local features of the candidate regions. The LDED uses two parameters: region ellipticity and local pattern characteristics to distinguish the melanocytes from the candidate nuclei regions. Experimental results on 28 dierent histopathological images of skin tissue with dierent zooming factors show that the proposed technique provides a superior performance.

In this paper, we propose a robust technique for temporal alignment of video sequences with similar planar motions acquired using uncalibrated cameras. In this technique, we model the motion-based video temporal alignment problem as a spatio-temporal discrete trajectory point sets alignment problem. First, the trajectory of the object of interest is tracked throughout the videos. A probabilistic method is then developed to calculate the `soft' spatial correspondence between the trajectory point sets. Next, a dynamic time warping technique (DTW) is applied to the spatial correspondence information to compute the temporal alignment of the videos. The experimental results show that the proposed technique provides a superior performance over existing techniques for videos with similar trajectory patterns.

The 14 papers in this special issue are extended versions of papers presented at ICME 2011, held in Barcelona, Spain, on 11-15 July 2011.

In this paper, a novel technique is proposed for temporal alignment of video sequences with similar motions acquired using uncalibrated cameras. In this technique, we model the motion-based video temporal alignment problem as a spatial-temporal discrete trajectory point sets alignment problem. The trajectory of the interested object is tracked though out the videos. A probabilistic method is then developed to calculate the spatial correspondence between trajectory point sets. Next, the dynamic time warping technique (DTW) is applied to the spatial correspondence information to compute the temporal alignment of the videos. The experimental results show that the proposed technique provides a superior performance over existing techniques for videos with planar motion.

Identification of singular points in the ridge structure is an important problem in fingerprint matching. This paper presents a fingerprint singular point detection method that is applicable to various fingerprint images regardless of their resolutions. Using the Discrete Hodge Helmholtz Decomposition (DHHD) method, potential structures of singular points can be extracted. We also calculate Poincare Index (PI) of the image. Then we combine DHHD and PI to detect singular points. The result of the experiment on the public databases with different images demonstrates that the proposed method has high accuracy in locating singular points.

Fluid vector flow (FVF) is a recently developed edge-based parametric active contour model for segmentation. By keeping its merits of large capture range and ability to handle acute concave shapes, we improved the model from two aspects: edge leakage and control point selection. Experimental results of cavity segmentation in chest radiographs show that the proposed method provides at least 8% improvement over the original FVF method.

This paper proposes a computational technique for estimating parameters of probability density functions (PDFs) governing marine noise, using Beaulieu series. The PDFs are assumed to be mixtures-of-Gaussians from the widely used Middleton's Class A model. The Beaulieu series for such PDFs are derived. Such an approach is orders of magnitude more efficient than approaches based on convolution integrals, and can be done in real-time. The computational complexity for the technique is then derived. Numerical simulations of these estimates show the method to be robust with respect to perturbations prevailing in the ocean. These results make it obvious that such a procedure can be used in conjunction with existing detectors on sonar platforms.

In this paper we develop a confidence measure that can determine if a given set of samples is suitable for inclusion in the reconstruction of a higher resolution dataset. The confidence measure is formulated as a weighted combination of two well defined objective functions. We discuss the scope of the confidence measure and the two key factors that affect it: (i) non-uniformity of the samples and (ii) error in temporal registration. We also present a greedy iterative rank-based method that uses the confidence measure for reconstruction from multiple sample sets. The proposed method is evaluated with real video, audio and MRI data.

Recent advances in computer vision and pattern recognition have fueled numerous initiatives that aim to intelligently recognize human activities. In this paper, we propose an algorithm for nonintrusive human activity recognition. We use an adaptive background-foreground separation technique to extract motion information and generate silhouettes (foreground) from the input videos. We then derive directionality-based feature vectors (directional vectors) from the silhouette contours and use the distinct data distribution of directional vectors in a vector space for clustering and recognition. We also exploit the dynamic characteristic of human motion in order to smooth decisions over time and reduce errors in activity recognition. Our approach is monocular, tolerant to moderate view changes, and can be applied to both frontal and lateral views of most activities. Experiments with short and long video sequences show robust recognition under conditions of varying view angles, zoom depths, backgrounds, and frame rates.

We present a robust and computational low complex method to estimate the physical camera parameters, intrinsic and extrinsic, for scene shots captured by cameras applying pan, tilt, rotation, and zoom. These parameters are then used to split a sequence of frames into several subsequences in an optimal way to generate multiple sprites. Hereby, optimal means a minimal usage of memory while keeping or even improving the reconstruction quality of the scene background. Since wide angles between two frames of a scene shot cause geometrical distortions using a perspective mapping it is necessary to part the shot into several subsequences. In our approach it is not mandatory that all frames of a subsequence are adjacent frames in the original scene. Furthermore the angle-based classification allows frame reordering and makes our approach very powerful.

Temporal registration is the establishment of correspondence between two (or more) temporal frames of video sequences, or 3-D volume data. In this paper, we propose to use event dynamics, a property that is inherent to an event and is thus common to all acquisitions of the event, for both global and local temporal registration of video sequences in order to generate high temporal resolution video. We compare our approach to a widely used linear interpolation based temporal registration algorithm and demonstrate that in the case of low temporal acquisition rate, a global event dynamics based approach, such as ours, has smaller temporal registration error. We also present a unique application of our work in solving 3-D (2D + time) high temporal resolution medical data visualization problem.

The capability of creating video data from MRI has many advantages in visualization for medical practitioners, including (i) not subjecting patients to harmful radiations, (ii) being able to monitor patients at short inter-exam time intervals, and (iii) being able to capture 3D volume data. The quality and speed with which MRI data can be acquired, however, poses a challenge towards supporting good quality visualization. In this work we present results from our preliminary attempts at enhancing the temporal resolution of video captured via MRI. Our initial focus is on visualization of swallowing and associated problems that are broadly categorized as Dysphagia. We present a method to register data from multiple swallows to generate high temporal resolution MRI videos. 1.

A magnetic localization and orientation system is proposed for tracking wireless capsule endoscope. This system uses a small magnet enclosed in the capsule to serve as excitation source. When the capsule moves, the magnet establishes a static magnetic field around. With the magnetic sensor array composed of Honeywell 3-axis magnetic sensors, HMC1053, the magnetic intensities in some pre-determined spatial points can be detected, and the magnet's position and orientation parameters can be computed based on an algorithm. To initiate the system and obtain better tracking accuracy, we propose a calibration technique for the magnetic tracking system. The calibration includes sensitivity determination and nonlinearity adjustment, sensor center position and orientation adjustment. Based on the calibration procedures, the system can achieve satisfactory tracking accuracy with the average localization error 3.3 mm and the average orientation error 3.0deg

To build a new wireless robotic capsule endoscope with external guidance for controllable and interactive GI tract examination, a sensing system is needed for tracking 3D location and 2D orientation of the capsule movement. An appropriate sensing approach is to enclose a small permanent magnet in the capsule. The magnet establishes a magnetic field around the patient's body. With the sensing data of magnetic sensor array outside the patient's body, the 3D location and 2D orientation of the capsule can be calculated. Higher localization and orientation accuracy can be obtained if more sensors and proper optimization algorithm are applied. In this paper, different nonlinear optimization algorithms are evaluated, and we have found that Levenberg-Marquardt method provides higher accuracy and faster speed. Simulations were done for investigating the de-noise ability of this algorithm based on different sensor arrays. Furthermore, the real experiment shows that the results are satisfactory with high accuracy.

Human gesture recognition is an active topic of vision research which has applications in diverse fields such as collaborative virtual environments and robot teleoperation. We propose a novel method for the recognition of hand gestures, used by air marshals for steering aircraft on the runway, using the Radon transform. Various aspects of the algorithm, including acquisition, segmentation, labeling and recognition using the parametric Radon transform are addressed in this paper. A binary skeleton representation of the human subject is computed. The Radon transform is used to generate maxima corresponding to specific orientations of the skeletal representation. Feature vectors are extracted from the transform space by computing the normalized cumulative projections of the Radon transform on the angle axis. K-means clustering is then applied to recognize static gestures from the extracted features. This technique has the potential to provide information about the exact orientation of gesture segments and can find use in ground control of unmanned air vehicles. Experiments with image data corresponding to the various ground air traffic control gestures used in directing aircrafts, highlight the potential application of this approach.

In T. Kwon et al. (2003) the authors have developed call admission control and adaptive bandwidth allocation schemes for serving multimedia connections in cellular wireless networks with fixed cell capacity. The architecture considers an adaptive networking framework where the bandwidth of multimedia calls can be dynamically adjusted, and the proposed admission method works by enforcing an upper bound on the cell overload probability. In this paper we consider a similar adaptive framework, and devise call admission control and bandwidth allocation strategies to serve multimedia connections in a CDMA-based 3G cellular network. The architecture aims at maintaining the session's quality during both intra-cell and inter-cell user movements by limiting the cell overload probability. A novel aspect of our work is a method for exploiting a priori knowledge of user mobility patterns to estimate the cell overload probability after some prescribed prediction interval. Important properties of the devised method are proved analytically. Compared to a non-predictive admission control scheme, the obtained results show that the proposed scheme achieves a lower forced termination probability, and higher throughput while consuming less base station transmission energy

Image indexing and retrieval techniques are important for efficient management of visual databases. These techniques are generally developed based on the associated compression techniques. In the fractal domain, luminance offset and contrast scaling parameter are typically used as the fractal indices. However, luminance offset and contrast scaling parameter are strongly correlated. In this paper, we prove that range block mean and contrast scaling parameters are independent. Based on this independence, we propose four statistical indices for efficient image retrieval. In addition, we propose an efficient hierarchical indexing strategy based on the de and ac component analysis. Experimental results on a database of 416 texture images, created by decomposing 26 images, indicate that the proposed indices significantly improve the retrieval rate, compared to other retrieval methods.

Quality of service (QoS) and traffic engineering (TE) capabilities are two important criteria in today's networks for supporting real-time applications. Multiprotocol label switching (MPLS) plays a key role in IP networks by providing QoS and TE features. In this paper, we first propose an analytical label switched path (LSP) model associated with the TE issue that assumes two different LSP transmission mechanisms: layer 2 cut-through switching and layer 3 default routing. Secondly, we propose a new LSP traffic flow distribution optimization objective function to minimize the overall network transmission delay. The simulation results show that the proposed model minimizes the overall delay and distributes the traffic on each link evenly. In other words, the connection-oriented forwarding characteristics of layer 2 switching technology are achieved in the proposed model and the incorporated objective function while retaining the equally desirable flexibility and scalability of layer 3 routing.

A novel embedded wavelet coding algorithm for color image and video compression is proposed. A color image is first represented in YUV color space. The discrete wavelet transform of each of the three color components is then calculated separately. The luminance component is compressed with context-modeled wavelet difference reduction (CM-WDR). The chrominance components are compressed using the contextual information obtained during the coding of the luminance component. Our experimental results show that the proposed algorithm performs better than two latest wavelet-based benchmark schemes, namely CEZW and JPEG2000, in terms of color SNR and component PSNR.

The model coefficients for the case of the non-orthogonal basis in Jacquin's mappings are contrast scaling and luminance offset. After orthogonalization, the model coefficients become range block mean and contrast scaling. These two fractal coding algorithms have the same encoding procedure except that luminance offset is replaced by range block mean, however, their decoding algorithms are different. In this paper, we prove that the orthogonal decoding algorithm converges faster than the non-orthogonal algorithm, while the two decoding algorithms produce the same iteration series if the initial image is the range-averaged image and the step size equals the side-length of the range block.

In this paper, we present a technique to robustly transmit regions of interest in the JPEG2000 framework. The technique assumes a prioritized region-of-interest coding and optimally assigns channel protection to the coded data according to the importance of every packet in the final bit-stream. The mean energy of the transform coefficients contained in a packet and the distance of a packet from the region of interest determine the importance of every packet. The channel protection is achieved by means of a concatenation of a cyclic redundancy check outer coder and an inner rate-compatible convolutional coder. Simulation results performed over a Rayleigh fading channel show an improvement in the visual quality of the reconstructed images.

A method is proposed to encode multiple regions of interest in the JPEG2000 image-coding framework. The algorithm is based on the rearrangement of packets in the code-stream to place the regions of interest before the background coefficients. In order to improve the quality of the reconstructed image, partial background information is included with the regions of interest. The method makes use of a Gaussian priority distribution to assign different priority levels to background and region of interest packets. The priority level is in turn used to determine how much background information should be included with the regions of interest. The proposed technique is fully compatible with the current JPEG2000 standard and allows transmission of different regions of interest with different priorities. Experimental results demonstrating the validity of the proposed approach are presented and compared with existing region of interest coding techniques.

An adaptive unequal channel protection technique is proposed for JPEG2000 compressed images exploiting the hierarchical structure of the coded bit stream. The proposed technique takes into account the effect of the channel errors in different packets of the bit stream in order to optimally protect the coded data according to the channel conditions. The robustness of the proposed technique is evaluated over a Rayleigh-fading channel with a concatenation of a cyclic redundancy check code and a rate-compatible convolutional code. Comparisons are made with the cases of equal channel protection and unequal channel protection across the layers. Simulation results show a significant improvement in subjective and objective quality of the received images.

Image indexing and retrieval techniques are important for efficient management of visual databases. These techniques are generally developed based on the associated compression techniques. In the fractal domain, the luminance offset and contrast scaling parameters are typically used as the fractal index. We propose to use the range block mean and contrast scaling as the fractal index. The image retrieval is performed in two steps. First, a coarse search is performed using the histogram of the range block means. Subsequently, a fine search is performed using the 2D joint histogram of the range block mean and contrast scaling parameters. Experimental results on a database of 416 texture images indicate that the proposed indices significantly improve the retrieval rate, compared to other retrieval methods.

A new fractal decoding algorithm based on range block mean and contrast scaling is presented. As a result, the decoding image is represented as the sum of the DC and AC components. We prove that new decoding algorithm converges faster than existing ones. Experiments show that the new decoding algorithm converges at least 2-3 times faster than existing methods.

This paper presents a new embedded wavelet image compression algorithm that is based on the wavelet difference reduction (WDR) method developed by Tian and Wells. While WDR employs a fixed order on scanning of the coefficients in a wavelet pyramid, the proposed algorithm employs an adaptive scanning order that results from partitioning each bit-plane into multiple fractional bit-planes by exploiting intra-subband and inter-subband correlation. Arithmetic coding (AC) in WDR is avoided in the proposed coder, resulting in lower complexity. These approaches enable the proposed algorithm to be conceptually simple and outperform both WDR and SPIHT (without AC) by Said and Pearlman in a rate-distortion sense. The PSNR improvement over WDR ranges from 0.1 to 1.5 dB on the set of test images at various bitrates.

An image can be characterized by its fractal parameters, and hence, the fractal parameters can be used as the image signature to retrieve the images. In this paper, based on the principle that fractal transform is completely determined by luminance offset and contrast scaling, we first propose histogram of luminance offset as a statistical index, and we further propose three composite indices by combining individual histograms to enhance retrieval rate and reduce computational complexity. Experimental results on a database of 416 texture images indicate that the proposed indices significantly improve the retrieval rate, compared to other retrieval methods.

We propose a reduced complexity wavelet-based image coding technique. Here, 64-D (for three stages of decomposition) vectors are formed by combining appropriate coefficients from the wavelet subimages, 16-D feature vectors are then extracted from the 64-D vectors on which vector quantization (VQ) is performed. At the decoder, 64-D vectors are reconstructed using a nonlinear interpolative technique. The proposed technique has a reduced complexity and has the potential to provide a superior coding performance when the codebook is generated using the training vectors drawn from similar images.

This paper presents a VLSI implementation of discrete wavelet transform (DWT). The architecture is simple, modular, and cascadable for computation of one or multidimensional DWT. It comprises of four basic units: input delay, filter, register bank, and control unit. The proposed architecture is systolic in nature and performs both high- and low-pass coefficient calculations with only one set of multipliers. In addition, it requires a small on-chip interface circuitry for interconnection to a standard communication bus. A detailed analysis of the effect of finite precision of data and wavelet filter coefficients on the accuracy of the DWT coefficients is presented. The architecture has been simulated in VLSI and has a hardware utilization efficiency of 87.5%. Being systolic in nature, the architecture can compute DWT at a data rate of N/spl times/10/sup 6/ samples/s corresponding to a clock speed of N MHz.