Universal Image Segmentation Technique for Cancer Detection in Medical Images

Tripty Singh, Siddharth Karanchery
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引用次数: 8

Abstract

Image segmentation is key area of research and focus due to the complexity of the problem statements. Based on the application and source of images the parameters of processing the image as well as the techniques employed to get efficient segmentation varies greatly. The works in this field thus far have been on a particular modality and utilized a sinlg e or combination of techniques for improving segmentation for those modalities.This paper focuses on a universal method of identification of cancerous regions in medical images captured from different image sources such as Computerized Tomography (CT) Scan, Magnetic Resonance Imaging (MRI), X-Ray and Ultrasound. The parameters utilized in this model have been optimized so as to avoid any and all human intercention in the identification of cancer in medical images. The proposed method employs a double mask technique in order to autoidentify region of interest without any manual intervention.
医学图像中癌症检测的通用图像分割技术
由于问题表述的复杂性,图像分割是研究和关注的关键领域。根据图像的用途和来源不同,处理图像的参数以及采用的有效分割技术也有很大的不同。到目前为止,这一领域的工作都是针对一种特定的模态,并利用一种或多种技术来改进对这些模态的分割。本文重点研究了从不同图像来源(如计算机断层扫描(CT)扫描,磁共振成像(MRI), x射线和超声)捕获的医学图像中识别癌症区域的通用方法。该模型使用的参数进行了优化,以避免任何和所有人为干预医学图像中的癌症识别。该方法采用双掩模技术,在不需要人工干预的情况下自动识别感兴趣的区域。
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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