Automatic Detection and Classification of Liver Lesions from CT-scan Images

Ria Benny, T. Thomas
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引用次数: 3

Abstract

This paper discusses about a method adopted to develop a computer-aided diagnostic system to achieve automatic detection and classification of liver lesions. The procedure followed consists of first segmenting the CT scan image so as to accurately extract out the lesion region alone from the rest of the abdominal details. This Region Of Interest(ROI) is now used up for extracting out first order and second order statistical feature values, which aids in the correct classification of lesions. The lesions can be classified into five types: normal liver, cysts, abscesses, benign growth (hemangioma, focal nodular hyperplasia, hepatocellular adenoma etc) and malignant growth (Hepatocellular Carcinoma, metastases etc), and this paper discusses a robust method for correctly identifying and classifying these lesions of the liver.
基于ct扫描图像的肝脏病变自动检测与分类
本文讨论了一种开发计算机辅助诊断系统的方法,实现肝脏病变的自动检测和分类。接下来的程序包括首先分割CT扫描图像,以便从其余腹部细节中准确地提取出病变区域。这个感兴趣区域(ROI)现在用于提取一阶和二阶统计特征值,这有助于正确分类病变。肝脏病变可分为五种类型:正常肝脏、囊肿、脓肿、良性生长(血管瘤、局灶性结节增生、肝细胞腺瘤等)和恶性生长(肝细胞癌、转移瘤等),本文讨论了正确识别和分类这些肝脏病变的可靠方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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