Automatic Pathology Detection for Chest X-ray Images Using Multiscale Intensity Texture Segmentation and classification

• Main Authors: Yong-Zhi Zeng, 曾詠智
• Other Authors: Hsien-Chu Wu
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/bde2y2

碩士 === 國立臺中科技大學 === 資訊管理系碩士班 === 105 === Digital image processing has been applied in medical domain widely, but the multitude still needs to manual processing. Automatic image segmentation and features analysis can assist doctor treatment and diagnose diseases more accurately, reduce the time of diagnosing and improve efficiency. Automatic medical image segmentation is difficult in that the image quality varied by equipment and dosage. In this thesis, the automatic method employed image multiscale intensity texture analysis and segmentation to surmount this problem. The proposed method automatically recognize and classify abnormal region without manual segmentation. Generally, automatic identification is based on the difference of the texture and organ shape, or any pathological changes of lung area. Therefore, the important features could be retained to identify abnormal areas. In this thesis, the chest x-ray images for finding whether lung region is healthy or not. The first proposed identifying common pneumothorax is based on SVM to classification method. Features are extracted from the lung image by the local binary pattern. Then, classification of pneumothorax lung is determined by support vector machines. The second proposed automatic pneumothorax detection is based on multiscale intensity texture segmentation. Remove the background and noises in the chest images for segmenting the lung of abnormal region. The segmenting the abnormal region. is used texture transforms from computing multiple overlapping blocks. Because the ribs boundaries are affected easily, the rib boundaries are identified by using Sobel edge detection. Finally, in order to obtain a complete disease region, the rib boundary is filled up in the rib boundary located between the abnormal regions.