Applications of Image Segmentation Techniques for Compression and Medical Image Processing
Date Issued
2011
Date
2011
Author(s)
Wang, Yu-Hsiang
Abstract
During the past few decades, image segmentation has been an important step from image processing to image analysis. The main purpose is to make a division of an image such that each category or region is homogeneous with respect to some measurements. The segmentation results can be useful for subsequent image processing treatment and widely applied to various researched fields, e.g. medical image analysis, image com-pression, object detection and matching, and video processing etc.
With the increasing size and number of medical images, automatically facilitating the image processing and analyzing by computer has become necessary. In particular, as a task of delineating anatomical structures and other regions of interest, image segmen-tation algorithms play a crucial role in numerous biomedical image applications such as the quantification of tissue volumes, diagnosis, study of anatomical structure, and com-puter-integrated surgery.
In this thesis, we propose an algorithm of muscle injury determination by image segmentation, which can directly find healthy and unhealthy muscle fibers from an ul-trasound image of muscle, and then derive the injury score. According to the injury score, the healthiness of the muscle can be judged and the degree of fibrosis, which is determined by the conventional method using coloring agent can be also estimated. The simulation results show that the injury score has high correlation with the fibrosis.
Besides, another biomedical algorithm (called cell counting) by image segmentation is proposed in this thesis. We apply morphology and the reflex angle operation to find out cell walls and separate each cell. Experiments show that our algorithm improves the existing problems of precision and time-consuming in previous methods.
In recent years, shape adaptive image coding has become a mainstream in many visual coding applications. The advantage of shape adaptive coding is that it can achieve a higher compression ratio because a segmented image region has high correlation of color values. However, the existing shape adaptive image compression techniques have the following drawbacks: high complexity and inefficient image coding.
Bearing in mind the above obstacle of compression, our image compression scheme based on the two dimensional orthogonal DCT expansion in triangular and trapezoid regions is proposed. The concept of the compression scheme is according to that any image segment can be viewed as an arbitrary polygon and a polygon can be composed by several triangular and trapezoid regions. Thus, the triangular and trapezoid segmentation algorithm is proposed in this thesis. The experimental results show that the trapezoids and triangles derived by our algorithm can nearly match an image segment. Furthermore, our image compression scheme achieves better performance than JPEG and other shape adaptive image compression standards.
Subjects
image segmentation
biomedical image processing
muscle injury
fibrosis
cell counting
image compression
shape adaptive image coding
Type
thesis
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