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This paper presents an algorithm to classify pixels in uterine cervix images into two classes, namely normal and abnormal tissues, and simultaneously select relevant features, using group sparsity. Because of the large variations in image appearance due to changes of illumination, specular reflections and other visual noise, the two classes have a strong overlap in feature space, whether features...
Populations of healthy older individuals are often highly heterogeneous, as prevalence of various underlying pathologies increases with age. Finding coherent groups of normal older adults may allow to identify subpopulations that are at risk of developing Alzheimer's disease (AD). In this paper, we propose an approach that utilizes longitudinal magnetic resonance imaging (MRI) data to obtain natural...
In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold...
Segmentation in echo-cardiographic images is a difficult task due to the presence of speckle noise, low contrast and blurring. We present a novel method based on clustering performed in the feature space. A new feature-based image representation is proposed. It is obtained by computing a local feature descriptor at every pixel location. This descriptor is derived using the Radon-Transform to effectively...
Breast cancer is the most frequent cancer and the most frequent cause of cancer induced death in women in the world. Diagnosis and prognosis of this cancer can be done through the radiological, surgical, and pathologic assessments of breast tissue samples. In developing countries, testing for detection of this cancer involves visual microscopic test of cytology samples such as Fine Needle Aspiration...
Wireless Capsule Endoscopy (WCE) is a state-of-the-art technology to examine the entire gastrointestinal tract. Its main disadvantage is long review time for physicians to diagnose diseases, as it will produce over 55,000 frames per patient for one examination. In this paper we propose a novel strategy to segment WCE video clips based on abnormality. The new scheme is based on a non-parametric corner...
A new technique for automatic extraction of object region and boundary from the background for cell nucleus segmentation of cervical cancer images is proposed in this work. Gradient magnitude and directional information are employed to extract the exact boundary of the object under consideration. Segmentation process begins with preprocess as computation of optimum threshold based on the clusters...
In content-based multimedia information retrieval, image and video information are usually described by high-dimensional vector, if using a linear scanning approach to search for those feature vectors will undoubtedly increase the cost of calculating, so the approximate vector indexing method and clustering method are used in conjunction in this article to make use of high-dimensional indexing techniques...
The extraction and matching of feature points is very important for measuring deformation fields of MR images. Current methods cannot extract and match enough feature points correctly when non-rigid soft biological tissues are deformed in MR images. The authors have therefore used SURF to extract feature points from initial MR images, utilizing every point in deformed MR images as feature points....
Acute lymphoblastic leukemia (ALL) is an serious hematological neoplasia of childhood which is characterized by abnormal growth and development of immature white blood cells (lymphoblasts). ALL makes around 80% of childhood leukemia and it mostly occur in the age group of 3-7. The nonspecific nature of the signs and symptoms of ALL often leads to wrong diagnosis. Diagnostic confusion is also posed...
Detection of outliers and relevant features are the most important process before classification. In this paper, a novel semi-supervised k-means clustering is proposed for outlier detection in mammogram classification. Initially the shape features are extracted from the digital mammograms, and k-means clustering is applied to cluster the features, the number of clusters is equal with the number of...
A cute lymphocytic leukemia (ALL) is a malignant disease characterized by the accumulation of lymphoblast in the bone marrow. An improved scheme for ALL detection in blood microscopic images is presented here. In this study features i.e. hausdorff dimension and contour signature are employed to classify a lymphocytic cell in the blood image into normal lymphocyte or lymphoblast (blasts). In addition...
This paper describes a method using image processing and genetic algorithm-neural network (GA-NN) for automated Mycobacterium tuberculosis detection in tissues. The proposed method can be used to assist pathologists in tuberculosis (TB) diagnosis from tissue sections and replace the conventional manual screening process, which is time-consuming and labour-intensive. The approach consists of image...
In this work we present a fully automated method for the accurate detection of cell nuclei boundaries in conventional Pap smear images, based on the watershed transform. For the extraction of nuclei and cytoplasm markers, which are used as starting points for the flooding process, a morphological reconstruction step is initially performed in each image. The watershed transform is then applied in the...
In quantitative positron emission tomography (PET) brain studies, the temporal dynamics of the radiopharmaceutical are usually analyzed separately for different brain structures. In a clinical environment, the delineation of brain structures is still often performed manually by human experts. In this study, we concentrate on automatic segmentation of the striatal brain structures (caudate, posterior...
In this study, a multi-level medical image semantic modeling approach based on fuzzy Bayesian networks is proposed. Its two forms are built. The one is a Bayesian network embedding Conditional Gaussian (CG) models, called BN-CG, and another is a Bayesian network embedding Gaussian mixture model (GMM), called BN-GMM. CG and GMM are employed to implement a fuzzy procedure to perform the soft quantification...
The progress of medical imaging technologies, from X-ray radiography, ultrasonic graph to modern age's Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan has helped the advance of the medical technology as well as the improvement of medical care quality all over the world. It is essential to promote our own medical imaging technologies so as to reduce the future overall medical expense...
A technique for intelligent processing is proposed for the analysis of brain magnetic resonance images. This paper presents segmentation and detection technique of tumor, edema and healthy tissues from fluid attenuated inversion recovery magnetic resonance images of brain with the help of composite feature vectors comprising of empirically developed functions of higher order wavelets and statistical...
Clustered micro calcifications (MCs) are one of the early signs of breast cancer. In this paper, we propose a new computer aided diagnosis (CAD) system for automatic detection of MCs in two steps. First, pixels corresponding to potential micro calcifications are found using a multilayer feed-forward neural network. The input of this network consists of 4 wavelet and 2 gray-level features. The output...
Attribute filters allow enhancement and extraction of features without distorting their borders, and never introduce new image features. These are highly desirable properties in biomedical imaging, where accurate shape analysis is paramount. However, setting the attribute-threshold parameters has to date only been done manually. This paper explores simple, fast and automated methods of computing attribute...
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