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Image segmentation algorithm and its application in the diagnosis of cancer cells

Author: GuoGe
Tutor: PingXiJian
School: PLA Information Engineering University
Course: Military Intelligence
Keywords: Image segmentation Cell image Iterative algorithm Watershed Esophageal cancer Feature extraction
CLC: R319
Type: Master's thesis
Year: 2005
Downloads: 224
Quote: 6
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Abstract


Image segmentation technology as an emerging discipline has been rapid development in just a few decades time, and widely applied to military, industrial, aerospace and other aspects. As the basis for image analysis and understanding, image segmentation is the most basic in the field of computer vision, one of the most difficult problems, the segmentation result is directly related to the subsequent performance of the algorithm. Due to the diversity and complexity of the image, there is not a completely generic segmentation method can achieve the correct segmentation of all images, image segmentation technology has been the research focus of the image processing. Cell image segmentation by computer image processing researchers attention as an important application areas of image segmentation, the cell image segmentation and recognition method along with the development of computer technology topics at the forefront of the field of contemporary image . With modern computer technology combined with tumor diagnosis specialist expertise, the use of image processing technology to medical image processing, in order to achieve recognition of esophageal cancer cells, classification, which has practical significance for medical research, and esophageal cancer early diagnosis and very broad prospects. This paper first reviews the definition and classification of image segmentation algorithm as well as existing gray-scale image segmentation algorithm, highlights some of the emerging segmentation techniques. Connection to target segmentation divided into two categories: one is no overlap of the target image segmentation, one is the overlap of the target image segmentation, these two different types of target image, respectively, to conduct research and propose two segmentation algorithm and its application to esophageal cancer cell image segmentation results demonstrate the effectiveness of the algorithm. No overlap of the target image segmentation algorithm study, focused on the the histogram valley point detection and threshold segmentation method based on iteration and the combination of these two algorithms a high stability of the automatic threshold segmentation algorithm. First detection histogram Valley point to get the number of threshold and the initial threshold, the initial threshold iterative method to optimize the final segmentation threshold. The method is capable of automatically determining the number of threshold values ??and iterative task fails due to the improper selection of the initial threshold value in the iterative method has been greatly improved, thereby improving the degree of automation of the segmentation, as well as the speed and stability of the iteration. Image overlapping objectives, the introduction of the concept of gray-scale difference transform propose a segmentation algorithm based on watershed transform. The algorithm, first the raw grayscale image grayscale differential transformation and distance transformation, the gradation difference FIG Distance FIG fusion, and the watershed transform fusion image as the reference image, in a certain extent inhibited the over-segmentation phenomenon, there are still a small amount of over-segmentation regional small region merging based on the t hypothesis testing method, to get the final segmentation result. Image segmentation purpose is to carry out target recognition, feature extraction as a from image segmentation Postrequisite for segmentation steps necessary to transition to the target recognition, so Finally, a brief discussion of relevant cell feature extraction, give some cancer cells characterized in parameters lay the foundation for further identification and given a pathological diagnosis of early esophageal cancer cell model for reference.

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