Analysis and Segmentation of the Bone Cancer MRI Image Based On Neural Networks Approach Model
Journal Title: IOSR journal of VLSI and Signal Processing - Year 2018, Vol 8, Issue 3
Abstract
- The manual segmentation of the Magnetic Resonance Imaging (MRI) bone image presents the following two issues: 1) it is tedious and time demanding task that can be only performed by a specialized clinician; and 2) it is prone to poor repeatability. These issues can be solve with the use of automatic bone image segmentation system, which has the potential to improve work flow in a in a clinical site and decrease the variability between user segmentations. This paper deals with segmentation of bone magnetic resonance imaging images based on semi supervised and dynamic model. The prime objective is to delineate the outline of an irregularity in an MRI image of the bone. Accurate and robust segmentation of bone tissue many employ applications such as surgery and radio therapy. It has been successful in segmenting the bone in every images acquired from several different MRI scanners, using different ego sequences. By using semi supervised method used to reduce the dependence on a rich initial training set and dynamic model to decrease the search complexity. The performance of and approach is calculated using a data set of diseased cases containing 100 annotated images and another data set of normal cases containing 20 annotated images. Further these techniques are also used to find irregularities in lung images.
Authors and Affiliations
Dr. K. Baskaran1 ,, Dr. R. Malathi2
Keywords
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