用惯性和最小化法自动检测乳房x光片中的不透明度

G. Kom , A. Tiedeu , M. Kom , C. Nguemgne , J. Gonsu
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引用次数: 6

摘要

本文提出了一种新的乳房x线图像可疑肿块区域检测算法。该算法采用直方图修正增强技术和基于惯性和最小化的分割方法。直方图修改滤波器的设计目的是通过清除无关的背景杂波来增强疑似肿块的疾病模式。然后对增强图像进行分割,利用图像强度类的惯性和最小化来定位可疑的质量区域。所提出的算法在雅温顿妇产科和儿科医院提供的32张乳房x光片数据库中进行了测试,这些肿块以前已由经验丰富的放射科医生定位。结果表明,该算法能够在所有情况下对质量进行识别,灵敏度约为94%。此外,我们发现检测到的肿块的大小和边缘与放射科医生的标记相似。此外,在某些情况下,我们可以检测到一些放射科医生无法标记出来的隐藏肿块。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Détection automatique des opacités dans les mammographies par la méthode de minimisation de la somme de l'inertie

In this paper a new algorithm for detection of suspicious mass area from mammographic images is presented. It uses histogram modification enhancement technique and a segmentation method based on minimization of inertia sum. The histogram modification filter is designed so as to be able to enhance disease patterns of suspected masses by cleaning up unrelated background clutters. Segmentation is then performed on the enhanced-image to localize the suspected mass areas using minimisation of inertia sum of images intensity classes. The proposed algorithm was tested on a database of 32 mammogramms provided by Gynaeco-obstetric and pediatric hospital of Yaoundé on which masses had previously been localised by experienced radiologists. Results show that the algorithm is able to identify masses in all cases presented with a sensibility of 94% approximately. In addition, we found out that sizes and edges of masses detected are similar to those marked by radiologists. Furthermore in some cases, we could detect some hidden masses that the radiologists were not able to mark out.

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