Fasil Fenta has completed M.Sc. and B. Sc. in computer science from University of Gondar. He is working as a lecturer in University of Gondar, department of Computer Science. He has done a research in digital images processing specifically digital image segmentation. Currently he has submitted two research papers in the international scientific journals and the papers are under review.


Image segmentation is often described as partitioning an image into a finite number of semantically non-overlapping regions. In medical applications, it is a fundamental process in most systems that support medical diagnosis, surgical planning and treatments. Today’s typical hospital environment is well-equipped with medical scanners that routinely provide valuable information to aid with the diagnosis or treatment planning for a particular patient. Computerized tomography (CT), magnetic resonance imaging (MRI), ultrasound, and positron emission tomography (PET) are examples of imaging modalities that are frequently used. Generally, this process is done manually by Radiologist, which may be time-consuming and tedious. To alleviate the problems, a number of segmentation algorithms have been proposed in different literatures. In this paper a hybrid segmentation technique is proposed for Radiology Images. This paper describes an algorithm to separate the lung tissue from a Chest CT to reduce the amount of data that needs to be analyzed. The main goal is to have a fully automatic algorithm for segmenting the lung tissue, and to separate the two lung sides as well. N-cut algorithm is used to segment the lungs. Digital image cleaning techniques are performed to remove air, noise and airways. Finally, a sequence of morphological operations is used to smooth the irregular boundary. The database used for evaluation is taken from a radiology-teaching file. Based on the evaluation result the applied segmentation algorithm works on a large number of different cases.