The neurodegenerative diseases are modelled by the deformation of the brain neurons. In the detection of neurodegenerative diseases including Parkinson's Disease (PD), the Three-Dimensional Magnetic Resonance Imaging (3D-MRI) has been utilized, recently. In this paper, by using a Voxel-Based Morphometry (VBM) method, the morphological alterations between the Structural MRI (sMRI) data of 40PD and 40 Healthy Controls (HCs) have been determined. By using the structural alterations between the PD patients and HC and two sample t-test method, the 3D Gray Matter (GM) and White Matter (WM) tissue masks are obtained separately for two different hypotheses, t-contrast and f-contrast. The Feature Selection and Kernel Learning for Local Learning-based Clustering (LLCFS) method is used to rank the features and a Fisher criterion algorithm is utilized to determine the number of the top-ranked features. The selected features are classified by using two classification approaches, namely Support Vector Machines (SVM) and Naive Bayes (NB). The results indicate that the classification performances of both NB and SVM methods with f-contrast outperform that with t-contrast for all GM, WM, and the concatenation of GM and WM tissue volumes. Additionally, the classification performance of SVM is higher than that of NB for all GM, WM, and the combination of GM and WM tissues. The highest area under curve results are obtained as 75.63%, 85.00%, and 90.00% for GM, WM, and the concatenation of them, respectively.

In many image segmentation problems involving limited and low-quality data, employing statistical prior information about the shapes of the objects to be segmented can significantly improve the segmentation result. However, defining probability densities in the space of shapes is an open and challenging problem, especially if the object to be segmented comes from a shape density involving multiple modes (classes). Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. In these methods, the evolving curve may converge to a shape from a wrong mode of the posterior density when the observed intensities provide very little information about the object boundaries. In such scenarios, employing both shape- and class-dependent discriminative feature priors can aid the segmentation process. Such features may involve, e.g., intensity-based, textural, or geometric information about the objects to be segmented. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors constructed by Parzen density estimation. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on a variety of synthetic and real data sets from several fields involving multimodal shape densities. Experimental results demonstrate the potential of the proposed method.

Content-based multimedia retrieval (CBMR) has been an active research domain since the mid 1990s. In medicine visual retrieval started later and has mostly remained a research instrument and less a clinical tool. The limited size of data sets due to privacy constraints is often mentioned as reason for these limitations. Nevertheless, much work has been done in CBMR, including the availability of increasingly large data sets and scientific challenges. Annotated data sets and clinical data for images have now become available and can be combined for multi-modal retrieval. Much has been learned on user behavior and application scenarios. This text is motivated by the advances in medical image analysis and the availability of public large data sets that often include clinical data. It is a systematic review of recent work (concentrating on the period 2011-2017) on multi-modal CBMR and image understanding in the medical domain, where image understanding includes techniques such as detection, localization, and classification for leveraging visual content. With the objective of summarizing the current state of research for multimedia researchers outside the medical field, the text provides ways to get data sets and identifies current limitations and promising research directions. The text highlights advances in the past six years and a trend to use larger scale training data and deep learning approaches that can replace/complement hand-crafted features. Using images alone will likely only work in limited domains but combining multiple sources of data for multi-modal retrieval has the biggest chances of success, particularly for clinical impact.

Statistical characterization of morphological changes of dendritic spines is becoming of crucial interest in the field of neurobiology. Automatic detection and segmentation of dendritic spines promises significant reductions on the time spent by the scientists and reduces the subjectivity concerns. In this paper, we present two approaches for automated detection of dendritic spines in 2-photon laser scanning microscopy (2pLSM) images. The first method combines the idea of dot enhancement filters with information from the dendritic skeleton. The second method learns an SVM classifier by utilizing some pre-labeled SIFT feature descriptors and uses the classifier to detect dendritic spines in new images. For the segmentation of detected spines, we employ a watershed-variational segmentation algorithm. We evaluate the proposed approaches by comparing with manual segmentations of domain experts and the results of a noncommercial software, NeuronIQ. Our methods produce promising detection rate with high segmentation accuracy thus can serve as a useful tool for spine analysis.

Advances in sensor technology, processing speed, high-speed networking, and the massive digital storages are being incorporated into today's healthcare practice. Tremendous amounts of medical multimedia data are captured and recorded in digital format during the daily clinical practice, medical research, and education. Intelligent medical knowledge discovery and retrieval from medical multimedia data is very useful and highly desirable. Motivated by the huge potential benefits, we have organized the ACM International Workshop on Medical Multimedia Analysis and Retrieval (MMAR2011), held in conjunction with The Annual ACM International Conference on Multimedia (ACM Multimedia 2011). In this paper, we first introduce the background and overview of the workshop. Then we introduce the workshop review process, followed with a brief introduction of the accepted papers in this workshop, as well as the conclusion.

Segmentation of brain structures from MR images is crucial in understanding the disease progress, diagnosis, and treatment monitoring. Atlases, showing the expected locations of the structures, are commonly used to start and guide the segmentation process. In many cases, the quality of the atlas may have a significant effect in the final result. In the literature, commonly used atlases may be obtained from one subject's data, only from the healthy, or depict only certain structures that limit their accuracy. Anatomical variations, pathologies, imaging artifacts all could aggravate the problems related to application of atlases. In this paper, we propose to use multiple atlases that are sufficiently different from each other as much as possible to handle such problems. To this effect, we have built a library of atlases and computed their similarity values to each other. Our study showed that the existing atlases have varying levels of similarity for different structures.

The aging population and the growing amount of medical data have increased the need for automated tools in the neurology departments. Although the researchers have been developing computerized methods to help the medical expert, these efforts have primarily emphasized to improve the effectiveness in single patient data, such as computing a brain lesion size. However, patient-to-patient comparison that should help improve diagnosis and therapy has not received much attention. To this effect, this paper introduces a fast and robust region-of-interest retrieval method for brain MR images. We make the following various contributions to the domains of brain MR image analysis, and search and retrieval system: 1) we show the potential and robustness of local structure information in the search and retrieval of brain MR images; 2) we provide analysis of two complementary features, local binary patterns (LBPs) and Kanade-Lucas-Tomasi feature points, and their comparison with a baseline method; 3) we show that incorporating spatial context in the features substantially improves accuracy; and 4) we automatically extract dominant LBPs and demonstrate their effectiveness relative to the conventional LBP approach. Comprehensive experiments on real and simulated datasets revealed that dominant LBPs with spatial context is robust to geometric deformations and intensity variations, and have high accuracy and speed even in pathological cases. The proposed method can not only aid the medical expert in disease diagnosis, or be used in scout (localizer) scans for optimization of acquisition parameters, but also supports low-power handheld devices.

This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and intershape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance images. We present a set of 2-D and 3-D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy.

In the medical domain, experts usually look at specific anatomical structures to identify the cause of a pathology, and therefore they can largely benefit from automated tools that retrieve relevant slice(s) from a patient's image volume in diagnosis. Accordingly, this paper introduces a novel search and retrieval work for finding relevant slices in brain MR (magnetic resonance) volumes. As intensity is non-standard in MR we explore performance of two complementary intensity invariant features, local binary patterns and Kanade-Lucas-Tomasi feature points, their extended versions with spatial context, and a simple edge descriptor with spatial context. Experiments on real and simulated data showed that the local binary patterns with spatial context is fast, highly accurate, and robust to geometric deformations and intensity variations.

In this paper, a computer vision based system is introduced to automatically sort apple fruits. An artificial neural network segments the defected regions on fruit by pixel-wise processing. Statistical features are extracted from the defected regions and then fruit is graded by a supervised classifier. Linear discriminant, nearest neighbor, fuzzy nearest neighbor, adaboost and support vector machines classifiers are tested for fruit grading, where the last two are found to perform best with 90 % recognition.