Novel Shape Description for CBIR in Medical Application

Abstract. Image Retrieval for Medical Applications (IRMA) has received a significant research interest over the past decade as a promising approach to address the data management challenges posed by the rapidly increasing volume of medical image data collections in use and also to aid clinical medicine, research, and education relying on visual content in the data. The research presented in this paper was aimed to improve the retrieval performance of an images retrieval system in medical applications based on shape features. In general, the work consists of two phases: (1) enrollment phase, which consist of feature extraction based on developed method to extract the shape features, (2) retrieving phase, which use the Euclidian distance measure. The conducted tests were carried on 350 medical images from four types (i.e., abdominal CT scan, MRI, ultrasonic, X-ray) and give good precision and recall rates (94,89)

An Online Content Based Email Attachments Retrieval System

E-mail is one of the most popular programs used by most people today. As a result of the continuous daily use, thousands of messages are accumulated in the electronic box of most individuals, which make it difficult for them after a period of time to retrieve the attachments of these messages. Most Email providers constantly improved their search technology, but till now there is something could not be done; i.e., searching inside attachments. Some email providers like Gmail has added searching words inside attachments for some file types (.pdf files, .doc documents, .ppt presentations) but for image files this feature not supported till now. However, E-mail providers and even modern researchers have not focused on retrieving the image attachments in the E- mail box. The paper was aimed to introduce a novel idea of using Content based Image Retrieval (CBIR) in E-mail application to retrieve images from email attachments based on entire contents. The work main phases are: feature extraction based on color features and connect to Email server to read Emails, the second phase is retrieving similar image attachments. The tests carried on email inbox contain 100 messages with 500 image attachments and gave good precision and recall rates When the threshold value is less than or equal to 0.4