| Summary: | 碩士 === 國立臺中技術學院 === 資訊科技與應用研究所 === 95 === The main purpose of this thesis is to develop contour detection and segmentation techniques based on edge features for medical images. In order to accurately analyze the information on normal/abnormal cervical cells and protein, the proposed methods segment cytoplasm and cell nucleus of a cervical cell image and protein spots contour of a 2-D electrophoresis (2D gel) image, respectively.
Biomedical image processing is one of the most frequently techniques in clinical diagnosis. Computer aided reading and judgment reduces manual ignorance and improves the judgment performance when doctors diagnosis. In this thesis, cervical cell images and 2-D electrophoresis (2D gel) images are the major research subjects to be analyzed. The difference between cervical cell images and 2-D electrophoresis images is the cervical cell image has a single cell within cytoplasm and nucleus in high image quality; the 2-D electrophoresis image contains lots of proteins in various colors and shapes. Due to different image characteristics, this thesis proposes different kinds of object detection and segmentation techniques for suiting to cervical cell images and 2-D gel images.
For the detection and segmentation of the cervical cell image, the mean filter is used to remove the noise of the cervical cell image. Then, an edge enhancement technique using the coarseness of each pixel and the gradient of the nearby two-group method for enhancing object edges is proposed. Then, Sobel edge detection is used to detect the edge information of the objects: Calculate the gradient of the cervical image and use thinning to express the edges in linear shape with non-maximum suppression. Then, Hysteresis thresholding is used to determine the possible object edge contour, and HTMS is applied to obtain edge skeleton. At last, watershed regional segmentation is used to determine whether edges belong to closed curve objects. Finally, the characteristics of cervical cell image edge lines are defined: The length of the edge lines of the two contours and edge lines of the contour being simple curve to determine the methods of cervical cell smear images. The experimental results are compared with that of Otsu and level set as cervical cell smear image periblast and cell nucleus contour segmentation. Also, the performance of mage segmentation is compared. The results show that the proposed method of this thesis can effectively and precisely offer cervical cell image segmentation.
The 2D gel image detection and segmentation are preprocesses which segment the protein points to determine their coordinate locations, sizes, shapes and density before diagnosis analyzing. Based on the cervical cell image detection technology, the initial edge image of 2D gel can be obtained by above techniques, and logarithm function is applied on the gradient of the initial edge image for edge enhancement. In order to effectively obtain the complete edges for segmenting the protein points, the contours of the edge image are classified to five different kinds of contours: Simple close contour, approximate close contour, fracture contour, neighbor contour and approximate contour determination for not among two extreme points. At last, above contours are further dealt with spot filter contour for obtaining compete contours, the spot filter contour including approximate contour determination, mend fracture contour, neighbor contour processing and approximate contour determination for not among two extreme points. From the experimental results, the edge detection of the proposed technique is compared with the six traditional methods which are Sobel, Canny, Roberts, Prewitt, Laplacian of Gaussian and Zero-cross for deviation analysis. Besides, the proposed contour determination method is also compared with level set method. Moreover, to emphasize the adaptability of this method, the proposed method compared with the software “ImageMaster” on contour segmentation for the performance. Finally, the experimental results show that the proposed method could precisely segments the 2D gel images.
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