Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering

Mohapatra, Subrajeet; Patra, Dipti; Kumar, K. Vinod

doi:10.5402/2012/923946

Cited by 18 publications

(16 citation statements)

References 31 publications

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“…Leukemia is detected based on the number, type, and proportion of various cell types in the blood. Segmentation algorithms enable identification of individual cells from smear images (Figure 2b); these algorithms can distinguish overlapping cells from individual cells in order to extract cell-based features and can also divide each WBC into its components: cell membrane, nucleus, and cytoplasm (Figure 2c) [19,[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. Following segmentation, metrics can be extracted from the WBCs and their subcellular components (cell size, nuclear size, etc.…”

Section: Digital Cytology Analysismentioning

confidence: 99%

Quantitative-Morphological and Cytological Analyses in Leukemia

Lantos¹,

Kornblau²,

Qutub³

2018

Hematology - Latest Research and Clinical Advances

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Leukemia, a blood cancer originating in the bone marrow, presents as a heterogeneous disease with highly variable survival rates. Leukemia is classified into major types based on the rate of cancerous cell growth and cell lineage: chronic or acute and myeloid or lymphoid leukemia. Histological and cytological analysis of the peripheral blood and the bone marrow can classify these major leukemia categories. However, histological analyses of patient biopsies and cytological microscopic assessment of blood and bone marrow smears are insufficient to diagnose leukemia subtypes and to direct therapy. Hence, more expensive and time-consuming diagnostic tools routinely complement histological-cytological analysis during a patient's diagnosis. To extract more accurate and detailed information from patient tissue samples, digital pathology is emerging as a powerful tool to enhance biopsy-and smear-based decisions. Furthermore, digital pathology methods integrated with advances in machine learning enable new diagnostic features from leukemia patients' histological and cytological slides and optimize patient classification, thus providing a cheaper, more robust, and faster diagnostic tool than current standards. This review summarizes emerging approaches to automatically diagnose leukemia from morphological and cytological-histological analyses.

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Section: Digital Cytology Analysismentioning

confidence: 99%

Quantitative-Morphological and Cytological Analyses in Leukemia

Lantos¹,

Kornblau²,

Qutub³

2018

Hematology - Latest Research and Clinical Advances

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“…Besides the above-mentioned techniques, clustering methods recently received much research attention for segmentation of microscopic blood images. Several clustering techniques were investigated by Mohapatra and co-researchers 6 9 28 29 . As an example, Mohapatra et al .…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

Neoh

Srisukkham

Zhang

et al. 2015

Sci Rep

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This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.

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“…The aim of noise [4] reduction is to suppress the noise while preserving the important fine details and edges which are the boundary tow different regions. Image segmentation has been done based on the Artificial Intelligent (neural network, fuzzy set, and rough set) [40] and Evolutional approach (ant colony optimization, PSO) as a filter on both noisy and noise free images.…”

Section: Fig 1 Classification Of Imagementioning

confidence: 99%

A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach

Jaiswal¹

2013

IOSR-JCE

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In image analysis, segmentation is the partitioning of a digital image into multiple regions (sets of pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in literature and there are a wide variety of approaches that are used. Different approaches are suited to different types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to the fact that there may be much correct segmentation for a single image. Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest domain-independent abstraction of an input image. Image segmentation is an important processing step in many image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular image or set of images, or more generally, for a whole class of images. In this paper, The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach methods that have been proposed in the literature. The rest of the paper is organized as follows. 1. Introduction, 2.Literature review, 3.Noteworthy contributions in the field of proposed work, 4.Proposed Methodology, 5.Expected outcome of the proposed research work, 6.Conclusion.

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Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering

Cited by 18 publications

References 31 publications

Quantitative-Morphological and Cytological Analyses in Leukemia

Quantitative-Morphological and Cytological Analyses in Leukemia

An Intelligent Decision Support System for Leukaemia Diagnosis using Microscopic Blood Images

A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach

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