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Cassification of breast tumor based on ultrasound images

机译:基于超声图像的乳腺肿瘤分类

摘要

This research primarily focuses on the predictive technology of identifying the state of tumors in the breast tissues. In breast cancer diagnosis, patients are forced to undergo a series of biopsies just to identify and confirm on the state of tumor, as whether malignant or benign. In this research however, an algorithm will be developed using MATLAB Image Processing Toolbox to indentify the state of a tumor solely based on ultrasound images. Ultrasound images of breast tumors are imported into MATLAB and are passed through a set of filters to remove background noise. Next, the filtered images are run through a set of edge detection algorithms which identifies and defines the region of interest. The processed images are analyzed qualitatively and the following results are obtained; the analysis shows that malignant tumors have well defined boundaries while benign tumors have poorly defined boundaries. To test this theory, the algorithm is used to process another set of ultrasound images of unknown characteristics. The results were analyzed and classified into two groups; malignant and benign. The results are compared with the actual biopsy results from the IIUM Breast Cancer Research Institute, Kuantan and all the analyzed results matched the biopsy results. As a recommendation to improve this study, a quantitative analysis on the ultrasound images is carried out so that more accurate results can be obtained. If the development of this algorithm is proven to be a success, it would be used in every hospital throughout the country to diagnose patients with breast cancer.
机译:这项研究主要侧重于确定乳腺组织中肿瘤状态的预测技术。在乳腺癌的诊断中,患者被迫进行一系列活检,以鉴别和确认肿瘤的状态,如恶性或良性。但是,在这项研究中,将使用MATLAB Image Processing Toolbox开发一种仅基于超声图像来识别肿瘤状态的算法。乳腺肿瘤的超声图像被导入到MATLAB中,并通过一组过滤器以消除背景噪声。接下来,经过滤波的图像通过一组边缘检测算法运行,这些算法识别并定义了感兴趣的区域。对处理后的图像进行定性分析,得到以下结果;分析表明,恶性肿瘤边界清晰,良性肿瘤边界清晰。为了检验该理论,该算法用于处理另一组未知特性的超声图像。分析结果并将其分为两组:恶性和良性。将结果与关丹IIUM乳腺癌研究所的实际活检结果进行比较,所有分析结果均与活检结果相符。作为改进此研究的建议,对超声图像进行定量分析,以便获得更准确的结果。如果这种算法的开发被证明是成功的,那么它将在全国的每家医院中用于诊断乳腺癌患者。

著录项

  • 作者

    Perumal Devendran Pillai;

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  • 年度 2009
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