2 research outputs found
An Image Processing Framework for Breast Cancer Detection Using Multi-View Mammographic Images
Breast cancer is the leading cause of cancer death in women. The early phase of breast cancer is asymptomatic, without any signs or symptoms. The earlier breast cancer can be detected, the greater chance of cure. Early detection using screening mammography is a common step for detecting the presence of breast cancer. Many studies of computer-based using breast cancer detection have been done previously. However, the detection process for craniocaudal (CC) view and mediolateral oblique (MLO) view angles were done separately. This study aims to improve the detection performance for breast cancer diagnosis with CC and MLO view analysis. An image processing framework for multi-view screening was used to improve the diagnostic results rather than single-view. Image enhancement, segmentation, and feature extraction are all part of the framework provided in this study. The stages of image quality improvement are very important because the contrast of mammographic images is relatively low, so it often overlaps between cancer tissue and normal tissue. Texture-based segmentation utilizing the first-order local entropy approach was used to segment the images. The value of the radius and the region of probable cancer were calculated using the findings of feature extraction. The results of this study show the accuracy of breast cancer detection using CC and MLO views were 88.0% and 80.5% respectively. The proposed framework was useful in the diagnosis of breast cancer, that the detection results and features help clinicians in making treatment
Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach
The mammography image eccentric area is the breast density percentage
measurement. The technical challenge of quantification in radiology leads to
misinterpretation in screening. Data feedback from society, institutional, and industry
shows that quantification and segmentation frameworks have rapidly become the
primary methodologies for structuring and interpreting mammogram digital images.
Segmentation clustering algorithms have setbacks on overlapping clusters, proportion,
and multidimensional scaling to map and leverage the data. In combination,
mammogram quantification creates a long-standing focus area. The algorithm
proposed must reduce complexity and target data points distributed in iterative, and
boost cluster centroid merged into a single updating process to evade the large storage
requirement. The mammogram database's initial test segment is critical for evaluating
performance and determining the Area Under the Curve (AUC) to alias with medical
policy. In addition, a new image clustering algorithm anticipates the need for largescale
serial and parallel processing. There is no solution on the market, and it is
necessary to implement communication protocols between devices. Exploiting and
targeting utilization hardware tasks will further extend the prospect of improvement in
the cluster. Benchmarking their resources and performance is required. Finally, the
medical imperatives cluster was objectively validated using qualitative and
quantitative inspection. The proposed method should overcome the technical
challenges that radiologists face