Breast Tumor Detection

We read with interest "Breast Augmentation and Augmentation-Mastopexy With Local Anesthesia and Intravenous Sedation," by Colque and Eisemann. The article prompted us to share with your readers the novel ultrasound-guided anesthetic techniques we use in breast surgery.

The goal of this paper is to detect the breast cancer using neural networks. Image processing techniques play an important role in the diagnostics and detection of diseases and monitoring the patients having these diseases. Breast Cancer detection of medical images is one of the most important elements of this field. Because of low contrast and ambiguous the structure of the tumor cells in breast images, it is still a challenging task to automatically segment the breast tumors. Our method presents an innovative approach to the diagnosis of breast tumor incorporates with some noise removal functions, followed by improvement features and gain better characteristics of medical images for a right diagnosis using balance contrast enhancement techniques (BCET). The results of second stage is subjected to image segmentation using Fuzzy c-Means (FCM) clustering method and Thresholding method to segment the out boundaries of the breast and to locate the Breast Tumor boundaries (shape, area, spatial sizes, etc.) in the images. The third stage feature extraction using Discrete Wavelet Transform (DWT). Finally the artificial neural network will be used to classify the stage of Breast Tumor that is benign, malignant or normal. The early detection of Breast tumor will improves the chances of survival for the patient. Probabilistic Neural Network (PNN) with radial basis function will be employed to implement an automated breast tumor classification.

We read with interest "Breast Augmentation and Augmentation-Mastopexy With Local Anesthesia and Intravenous Sedation," by Colque and Eisemann. The article prompted us to share with your readers the novel ultrasound-guided anesthetic techniques we use in breast surgery.

A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths worldwide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. This paper reviews the processes and techniques used in detecting tumor based on medical imaging results such as mammograms, x-ray computed tomography (x-ray CT) and magnetic resonance imaging (MRI). We find that computer vision based techniques can identify tumors almost at an expert level in various types of medical imagery assisting in diagnosing myriad diseases.

Background: Pseudoangiomatous stromal hyperplasia is an uncommon mesenchymal breast neoplasm.
Case presentation: Here we present a case of an 11-year old hispanic girl affected by bilateral mammary nodular
pseudoangiomatous stromal hyperplasia, an uncommon breast disease, with a review of the literature related to
diagnostic workup, differential diagnosis, and management. A rapidly growing mass in the breast may be stressful for
both parents and child as the suspicion of malignancy arises. Multiple wide excisions of both breasts were performed.
Conclusions: The purpose of this case report is to draw attention to the fact that most emerging lesions of the breast
in girls during puberty are benign diseases.

This review focuses on different imaging techniques such as MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and timeconsuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research.