No abstract

Branchio-oto-renal syndrome (Melnick-Fraser Syndrome) is a rare Autosomal Dominant disorder characterized by the syndromic association of branchial cysts or fi stulae along with external, middle & inner malformations and renal anomalies. Incomplete penetrance and variable expressivity are common with the phenotypic variation ranging from mild to severe forms & consisting of various eye, ear, oral and craniofacial abnormalities. Mutations in the EYA1 gene on chromosomal site 8q13.3 are identifi ed as the primary cause of BOR syndrome. We present a 3year old child with BOR syndrome, who came to us with bilateral low set, malformed ears & profound cochlear hearing loss along with bilateral branchial fi stulae & unilateral renal agenesis. This child underwent successful cochlear implantation recently. The clinical presentation, pre-operative investigations, intra-operative fi ndings & post-op habilitation status are presented with special highlights on the unique facial nerve course along with middle and inner ear anomalies which posed a surgical challenge during cochlear implantation.

The objective of this paper is to explore an expedient image segmentation algorithm for medical images to curtail the physicians' interpretation of computer tomography (CT) scan images. Modern medical imaging modalities generate large images that are extremely grim to analyze manually. The consequences of segmentation algorithms rely on the exactitude and convergence time. At this moment, there is a compelling necessity to explore and implement new evolutionary algorithms to solve the problems associated with medical image segmentation. Lung cancer is the frequently diagnosed cancer across the world among men. Early detection of lung cancer navigates towards apposite treatment to save human lives. CT is one of the modest medical imaging methods to diagnose the lung cancer. In the present study, the performance of five optimization algorithms, namely, k-means clustering, k-median clustering, particle swarm optimization, inertia-weighted particle swarm optimization, and guaranteed convergence particle swarm optimization (GCPSO), to extract the tumor from the lung image has been implemented and analyzed. The performance of median, adaptive median, and average filters in the preprocessing stage was compared, and it was proved that the adaptive median filter is most suitable for medical CT images. Furthermore, the image contrast is enhanced by using adaptive histogram equalization. The preprocessed image with improved quality is subject to four algorithms. The practical results are verified for 20 sample images of the lung using MATLAB, and it was observed that the GCPSO has the highest accuracy of 95.89%.

Odontogenic cysts and tumors are well-recognized entities to the specialist oral pathologist and they seldom pose problems in differential diagnosis. This paper deals with an aggressive cystic lesion in the maxilla of a 65-year-old male that was characterized by a large radiographically multilocular lesion and a multicystic pattern microscopically. The categorization of this lesion was complicated by the presence of features suggestive of both glandular odontogenic cyst and cystic ameloblastoma with aggressive histologic phenotypes.

In diagnosis of diseases Ultrasonic devices are frequently used by healthcare professionals. The main problem during diagnosis is the distortion of visual signals obtained which is due to the consequence of the coherent of nature of the wave transmitted. These distortions are termed as 'Speckle Noise'. The present study focuses on proposing a technique to reduce speckle noise from ultrasonic devices. This technique uses a hybrid model that combines fourth order PDE based anisotropic diffusion, linked with SRAD filter and wavelet based BayesShrink technique. The proposed filter is compared with traditional filters and existing filters using anisotropic diffusion. Experimental results prove that the proposed method is efficient in reaching convergence quickly and producing quality denoised images.