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A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination

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Abstract

The early diagnosis of lymphatic system tumors heavily relies on the computerized morphological analysis of blood cells in microscopic specimen images. Automating this analysis necessarily requires an accurate segmentation of the cells themselves. In this paper, we propose a robust method for the automatic segmentation of microscopic images. Cell segmentation is achieved following a coarse-to-fine approach, which primarily consists in the rough identification of the blood cell and, then, in the refinement of the nucleus contours by means of a neural model. The method proposed has been applied to different case studies, revealing its actual feasibility.

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Authors and Affiliations

  1. Institute of Information Science and Technologies CNR, Via G. Moruzzi 1, 56124, Pisa, Italy

    S. Colantonio & O. Salvetti

  2. Dorodnicyn Computing Center, Russian Academy of Sciences, ul. Vavilova 40, Moscow, 119991, Russia

    I. B. Gurevich

Authors
  1. S. Colantonio
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  2. O. Salvetti
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  3. I. B. Gurevich
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Corresponding author

Correspondence to S. Colantonio.

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This article was submitted by the authors in English.

Sara Colantonio, M. Sc. honors degree in Computer Science from the University of Pisa in 2004, PhD student in Information Engineering at the Dept. of Information Engineering, Pisa University, is a research fellow at the Institute of Information Science and Technologies of the Italian National Research Council, in Pisa. She has a grant from Finmeccanica for studies in the field of image categorization with applications in medicine and quality control. Her main interests include neural networks, machine learning, industrial diagnostics, and medical imaging. She is a coauthor of more than fifteen scientific papers. At present, she is involved in a number of European research projects regarding image mining, information technology, and medical decision support systems.

Ovidio Salvetti, director of research at the Institute of Information Science and Technologies (ISTI) of the Italian National Research Council (CNR), in Pisa, is working in the field of theoretical and applied computer vision. His fields of research are image analysis and understanding, pictorial information systems, spatial modeling, and intelligent processes in computer vision.

He is a coauthor of four books and monographs and more than three hundred technical and scientific articles; he also possesses ten patents regarding systems and software tools for image processing. He has been a scientific coordinator of several national and European research and industrial projects, in collaboration with Italian and foreign research groups, in the fields of computer vision and high-performance computing for diagnostic imaging.

He is member of the editorial boards of the international journals Pattern Recognition and Image Analysis and G. Ronchi Foundation Acts. He is at present the CNR contact person in ERCIM (the European Research Consortium for Informatics and Mathematics) for the Working Group on Vision and Image Understanding, member of IEEE and of the steering committee of a number of EU projects. He is head of the ISTI Signals and Images Laboratory.

Igor B. Gurevich. Born 1938. Dr. Eng. [Diploma Engineer (Automatic Control and Electrical Engineering), 1961, Moscow Power Engineering Institute, Moscow, USSR]; Dr. (Theoretical Computer Science/Mathematical Cybernetics), 1975, Moscow Institute of Physics and Technology, Moscow, USSR. Head of department at the Dorodnicyn Computing Center of the Russian Academy of Sciences, Moscow; assistant professor at the Computer Science Faculty, Moscow State University. He has worked from 1960 to present as an engineer and researcher in industry, medicine, and universities and in the Russian Academy of Sciences. Area of expertise: image analysis, image understanding, mathematical theory of pattern recognition, theoretical computer science, pattern recognition and image analysis techniques for applications in medicine, nondestructive testing, process control, knowledge bases, knowledge-based systems. Two monographs (in coauthorship), 135 papers on pattern recognition, image analysis, theoretical computer science and applications in peer reviewed international and Russian journals, conference and workshop proceedings; one patent of the USSR, four patents of the RF Executive Secretary of the Russian Federation Association for Pattern Recognition and Image Analysis, member of the International Association for Pattern Recognition Governing Board (representative from the Russian Federation), IAPR fellow. He has been the PI of many research and development projects as part of national research (applied and basic research) programs of the Russian Academy of Sciences, of the Ministry of Education and Science of the Russian Federation, of the Russian Foundation for Basic Research, of the Soros Foundation, and of INTAS. Vice Editor-in-Chief of Pattern Recognition and Image Analysis, International Academic Publishing Company “Nauka/Interperiodica” Pleiades Publishing.

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Colantonio, S., Salvetti, O. & Gurevich, I.B. A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination. Pattern Recognit. Image Anal. 17, 428–437 (2007). https://doi.org/10.1134/S1054661807030108

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