Segmentation of B Using Abdel–Razzak Natsheh, Prasad VS Po Manchester * Traffo a.natsheh@mmu.ac.uk n.anani Abstract—The diagnosis of sinus diseases (e.g. requires the segmentation of bone structure sinus areas in CT (computer tomography) uniformity of bone tissue, ranging from dens textured spongy bone, the irregular shapes bones, and the small inter-bone spaces resolution of a CT image coupled with the vessels and the inherent blurring of CT im segmentation of a bone a challenging task. In technique is presented which uses hierarchi maps to segment the structure of bone surr regions. The algorithm has been successfull tested with various CT images that have disease. I. INTRODUCTION Diagnosis of a sinus condition is a proces use of multiple sources of information an laboratory and visual tests, computerized t scan images, and the expertise of a clinician sinus condition diagnoses is the quality and images that a clinician uses to make decision differ significantly in terms of quality and a The diagnoses can be further complicated by patients’ anatomy, the extent of diseased s bone structure. Discussions with ENT radiologists highlighted the need for an aut based analysis and diagnosis tool that can be an aid to experts, but also for use in the train medical students. Such a tool, the Sinus D henceforth referred to as the (SDS) has been techniques based on classical image processi procedures in conjunction with Artificial (ANNs) tools. Previous work reported the po using ANNs in enhancing early diagnosis [1 important stages in developing the propos image segmentation stage in extracting the b the CT images [1]. Medical image segmentation, however, h demanding task. This is true, in particular, fo of bones within the sinus areas in CT i general, the segmentation of bones in CT im a relatively straightforward task, segmen surrounding sinus areas is tricky becaus datasets contain irregularly shaped bones wit distances relative to the resolution of CT known fact is that bone tissue cannot Bone Structure in Sinus g Self-Organizing Maps nnapalli, Nader Anani, Dalil Benchebra, Atef El- r Metropolitan University, Manchester M1 5GD, UK ord General Hospital, Manchester M41 5SL, UK i@mmu.ac.uk , p.ponnapalli@mmu.ac.uk , atefelk malignant disease) e surrounding the images. The non- se cortical bone to of closely packed compared to the presence of blood maging, render the this paper, a novel ical self-organizing rounding the sinus ly applied to, and different types of ss that involves the nd skills such as tomography (CT)- n. A key feature of complexity of the ns. The images can angles of imaging. y the irregularity of sinus area and the consultants and tomated computer- e used, not only as ning of doctors and Diagnostic System, n developed using ing algorithms and Neural Networks otential benefits of ]. One of the most sed system is the one structure from has proved to be a or the segmentation mages. While, in mages is considered ntation of bones se the volumetric th small inter-bone imaging. A well be characterized uniformly: the outer layer of th denser than the spongy bone n eyes. Thus, under CT imaging smooth while spongy bone app blood vessels resemble the back surface of bone images. I characteristics translate into segmentation techniques (see f shell (between the eye and sin bone boundaries due to the imaging; (iii) textured areas co alternating between bone-like (iv) the narrow inter-bone regio Figure 1. Weak e While it is possible for me images using thresholding or ANALYZE package or simil intensive considering the time correction. Thus, the developm techniques that minimize user i number of algorithms have be purpose; edge detection method and watershed transformation [ However, the majority of t information about the expected some sort of user interaction purpose of the proposed system diagnosis process. Therefore combining two self-organizing hierarchical self-organizing ma s CT Images s -kholy * and Peter Norburn* kholy@hotmail.com he bone structure (skull bone) is near the sinus areas close to the g skull bone appears bright and pears dark and textured. Finally, kground that leads to gaps in the In the image domain, these four challenging areas for figure 1): (i) gaps in the cortical nus areas); (ii) weak or diffused partial volume effect in CT orresponding to the spongy bone and tissue-like intensities and ons which tend to be diffused. edges in CT image. edical experts to segment these manual seeding, e.g., using the lar tools, this process is labor e required for accurate manual ment and use of segmentation interaction is highly desirable. A een invoked and tested for this ds [2,3], active contours [4,5,6], 7,8]. the above methods require prior features of the image and hence n is required. This defies the m which aims at automating the , a new improved technique g maps (SOM), also known as ap (HSOM), has been developed 978-1-4244-6493-7/10/$26.00 ©2010 IEEE 290