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A weakly supervised image classification framework is presented in this paper. Given reference images marked by clinicians as relevant or irrelevant, we learn to automatically detect relevant patterns, i.e. patterns that only appear in relevant images. After training, relevant patterns are sought in unseen images in order to classify each image as relevant or irrelevant. No manual segmentations are...
We present a color segmentation approach based on a two-dimensional color map derived from the input image. Pathologists stain tissue biopsies with various colored dyes to see the expression of biomarkers. In these images, because of color variation due to inconsistencies in experimental procedures and lighting conditions, the segmentation used to analyze biological features is usually ad-hoc. Many...
We present a novel method for automated learning of features from unlabeled image patches for classification of tumor architecture. In contrast to previous manually-designed feature detectors (e.g., Gabor basis function), the proposed method utilizes inexpensive un-labeled data to construct features. The algorithm, also known as reconstruction independent subspace analysis, can be described as a two-layer...
Dilated capillaries are an important characteristic of basal cell carcinoma (BCC). Detecting capillaries in images can improve a computer-aided skin cancer diagnosis system. In this study, we investigate the feasibility to extract capillaries from clinical images of skin lesions recorded by a regular digital camera. First, we used a compact set of 1 curvilinear and 2 color parameters to train a support...
This paper presents an algorithm for classification of non-melanoma skin lesions based on a novel hierarchical K-Nearest Neighbors (K-NN) classifier. The K-NN classifier is simple, quick and effective. The hierarchical structure decomposes the classification task into a set of simpler problems, one at each node of the classification. Feature selection is embedded in the hierarchical framework that...
This paper proposes a method to track abnormalities in successive frames in a capsule endoscopic image sequence. Exact tracking of an abnormality in the gastrointestinal tract is useful in preparing the content for educational systems. However, if the abnormality is de-formable over continuous frames and its features are not highly distinct, it is difficult to track abnormalities precisely. The proposed...
Retinopathy of prematurity (ROP) is a vascular disease in premature infants. This is characterized by abnormal vessel growth and subsequent fibrosis in the peripheral retina. The prognosis of ROP relies on information on the presence of abnormal growth and their location. Diagnosis is based on a series of images obtained with a wide field of view camera (such as RetCam), to capture the complete retina...
Color nonstandardness — the propensity for similar objects to exhibit different color properties across images — poses a significant problem in the computerized analysis of histopathology. Though many papers propose means for improving color constancy, the vast majority assume image formation via reflective light instead of light transmission as in microscopy, and thus are inappropriate for histological...
Image segmentation of very large and complex microscopy images are challenging due to variability in the images and the need for algorithms to be robust, fast and able to incorporate various types of information and constraints in the segmentation model. In this paper we propose a graphical model based image segmentation framework that combines the information in images regions with the information...
The Cancer Genome Atlas (TCGA) provides a rich repository of whole mount tumor sections that are collected from different laboratories. However, there are a significant amount of technical and biological variations that impede analysis. We have developed a novel approach for nuclear segmentation in histology sections, which addresses the problem of technical and biological variations by incorporating...
Recent advances in the real-time 3D full volume echocardiography have enabled high frame rate acquisition of volumetric color Doppler flow images. In this paper, we propose a fully automated method for de-aliasing of color Doppler flow and quantification of the cardiac flow using instantaneous 3D+t ultrasound data. First, the anatomical information, such as mitral annulus and left ventricle outflow...
In this paper, we propose a new graph-based approach to address the problem of cytological computer-aided diagnostic. Such approach uses our previously introduced formalism of PdEs-based morphology and geometric diffusion on graphs. The approach is illustrated through two applications in cytopathology (involving Feulgen and Papanicolaou colorations), with examples of nucleus extraction and classification.
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