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Review on Image Segmentation Techniques Incorporated with Machine Learning in the Scrutinization of Leukemic Microscopic Stained Blood Smear Images

  • Duraiswamy Umamaheswari
  • Shanmugam Geetha
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

This paper is a contemplated work of N-different methods that have been employed in the area of revealing and classifying leukocytes and leukoblast cells. Blood cell images obtained through digital microscopes are taken as input to the algorithms reviewed. In bringing out the nucleus and cytoplasm of White Blood Cells (WBCs), the images have been undergone by a variety of image segmentation techniques along with filtering, enhancement, edge detection, feature extraction, classification, and image recognition steps. Apart from image processing, the analysis and categorization of the leukemic images are handled using some other machine learning techniques of computer science discipline. Assessment of accuracy and correctness of the proposals were done by applying texture, color, contour, morphological, geometrical, and statistical features.

Keywords

Leukemia Machine learning Medical image processing Review Segmentation WBC 

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Authors and Affiliations

  1. 1.Department of Computer ScienceVidyasagar College of Arts and ScienceUdumalpetIndia
  2. 2.Department of Computer ScienceGovernment Arts CollegeUdumalpetIndia

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