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基于小波变换和隐马尔可夫模型的乳腺x线影像乳腺癌肿瘤诊断

摘要

乳腺癌是15至54岁妇女死亡的最重要原因之一。每13分钟就有一名妇女死于乳腺癌，12.6%的妇女在一生中受到感染。虽然乳房x光检查是检测疾病最有效的方法之一;但它仍有不足和局限性。在乳房x线摄影图像的解释中，癌性病变可能无法诊断，或非癌性病变被检测为癌症。近年来，为了帮助放射科医生发现和诊断肿瘤肿块，利用图像处理科学对乳房x线摄影图像进行特征提取，提高了图像质量。这将提高检测速度和准确率。本文提出了一种基于小波和隐马尔可夫模型的乳腺癌肿瘤可疑区域检测新方法。将这两种方法结合起来，与以往工作的方法相比，提高了效率。在本研究中，肿瘤肿块被检测出来，肿瘤肿块的百分比也变得清晰; this makes to estimate mass growth rate. In this paper, Markov model with tree structure is used in order to extract statistical properties of wavelet transform components. Markov model has special ability in extracting information related to edges and protruding parts of image context due to its features which can accurately detect cancer areas. In this research we try to estimate the appropriate label (clustering) of pixels from a checked image in order to segment cancer areas. Certain joint distribution is assumed for pixels of a region or class; then, the maximum similarity of different areas of an image under review is checked using ML method. Combination of MIAS database and Paden including 150 images is used in order to test the proposed method. The results indicate that proposed method is more accurate compared to methods that use only wavelet transforms method. Detection rate of proposed method is 96% that is improved 24.5% compared to wavelet transform with detection rate of 71.5 percent.

Sayedeh Somayeh Hosaini, Mehran Emadi。