Document Type


Lead Author Type

CIS Masters Student


Dr. Jonathan Leidig,

Embargo Period



Medical images play a vital role in identifying diseases and detecting if organs are functioning correctly. Image processing related to medical images is an active research area in which various techniques are used in order to make diagnosis easier. The brain is a vital organ in our body, and brain tumors are a very critical life altering condition. Identifying tumors is a challenging task and various image processing techniques can be used. Doctors can identify tumors from looking at the scan, and this project attempts to automatically derive these results. In this project, image processing is done for automatically detecting the presence of brain tumors in a given brain scan. Content-based image retrieval extracts features from a query or template image, computes a measure of similarity, and gives results by detecting tumors. Template matching is used to identify a template at any position within the image to identify tumor location.

Secondly, early detection of Alzheimer’s, which in turn prevents dementia, can be determined from the presence of amyloid fluid along with the other factors. The amyloid fluid presence helps in detecting dementia at an early stage. The presence of this fluid can be found in a PET scan of the brain. Here, the idea is to show the color distribution from a scan image, i.e., the domination of given colors. Content-based image retrieval’s low level feature based approaches such as color histograms are used. In this project, the conventional K- means algorithm is used for clustering the histograms, and identifying dominant colors.