In this paper, we propose a general formulation of discrete functional regularization on weighted graphs. This framework can be used to on any multi-dimensional data living on graphs of arbitrary topologies. But, in this work, we focus on the microscopic image segmentation and classification with a semi and unsupervised schemes. Moreover, to provide a fast image segmentation we propose a graph ...

Background: Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), ...

Examination of tissue biopsy and quantification the various characteristics cellular processes are clinical benchmarks in cancer diagnosis. Nuclei glands instance segmentation greatly assists high-throughput process accurate appraisal biopsy. It subsequently makes a significant improvement to computational pathology for diagnosis, treatment planning, survival analysis. Recent advancements field...

A bottleneck of histopathology image segmentation is its execution speed and memory capacity for large images containing hundreds and thousands of objects, such as cells. In this paper we propose an approach to perform online nuclear segmentation on large fluorescence brain tumor images using CometCloud, an autonomic Cloud Engine. Based on our previously developed cellular segmentation algorith...

The purpose of the present thesis is the study of classification algorithms for the design of a pattern recognition system to characterize thyroid gland cancer to benign or malignant categories, based on the processing and analysis of cytological images. The process includes the digitization of microscopy images from the cytological material, in specially prepared specimens, processed with Hema...

Abstract In breast cancer image analysis, reliable segmentation of the nuclei is still an open-ended research problem. this paper, a new clustering-based method presented. First, proposed pre-processes histopathology through SLIC method. Then, novel variant multi-objective grey wolf optimizer employed to group obtained super-pixels into optimal clusters. Lastly, cluster with minimum value segme...