Segmentation of Breast Ultrasound images of Microcalcification

Soroush Mohammad Sharifi¹

Abstract :
Breast calcification is the deposition of calcium in breast tissue. Mammographic images show these deposits as white dots or lines and are usually so small that they cannot be felt. Breast calcifications are usually seen on mammograms and are much easier to diagnose after menopause. Although these calcifications are usually noncancerous (benign), certain patterns of breast calcification - such as tight clusters or abnormal shapes - can indicate breast cancer. If the pattern of calcification is suspected, the radiologist will recommend further tests such as re-mammography with magnified images or breast biopsy or ultrasound. The best way to tell if a tissue is cancerous is to use a biopsy and then evaluate if the problem is that it is invasive. Mammography is currently the most appropriate way to diagnose breast cancer. However, due to the type of breast tissue and the use of low-density X-rays in mammograms, the images have low contrast. Tumours also have different sizes and shapes, so it is very difficult and tedious to diagnose lesions, especially in the early stages of formation. The main purpose of this study is to segment the breast mass area based on selective patches. The results show a lower error rate in the application of the decision tree model in micro calcification segmentation than other previous methods
Keywords : Calcification , Breast , ultrasound imaging , segmentation , decision tree model

زبان مقاله : انگلیسی
محور مقاله : مهندسی پزشکی
محل انتشار : سومین کنگره بین المللی مهندسی، فناوری و نوآوری
آدرس وب سایت کنفرانس : eticong.com/3rd
ارسال کننده : سروش محمد شریفی
کد IOI مقاله : XDHC-GZACE
لینک مستقیم مقاله : https://isnac.ir/XDHC-GZACE