Segmentation of Pre-processed Medical Images: An Approach Based on Range Filter

Abstract

Medical image segmentation is a frequent processing step. Medical images are suffering from unrelated article and strong speckle noise. In this paper, we propose an approach to remove special markings such as arrow symbols and printed text along with medical image segmentation using range filter. The special markings are extracted using Sobel edge detection technique and then the intensity values of the detected markings are substituted by the intensity values of their corresponding neighborhood pixels. Next, three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. Finally range filter is applied to segment the texture content of different modalities of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed approach which lead to have precise content based medical image classification and retrieval systems.

Computational Analysis of Vertebral Body for Compression Fracture Using Texture and Shape Features

“…It is known that the foreground surface contours of the anatomical structure are known to display more texture. That is, the foreground pixels of the medical images have higher irregularity and thus have higher range values (Rajaei et al, 2012). Hence to this superimposed output, range filter is applied to rightly segment VBs that is normal, benign and malignant.…”

“…In this modern age, medical images are analysed for various automated tasks such as annotation, localization, labelling, segmentation, registration and classification all of which have become popular research topics. Computer Aided Diagnosis (CAD) acts as a practical tool to analyze any particular anatomical structure of a medical image to make better decisions in clinical diagnosis of pathologies, fractures, tumors, abnormalities and also for further planning of treatment, surgeries, post-surgical assessment and so forth (Rajaei et al, 2012). Medical institutions and hospitals produce immense medical images on a daily basis.…”

“…Medical images are well known to contain certain components which will affect the image contents such as unknown noise, artifacts, special markings (Rajaei et al, 2012), inhomogeneity, poor contrast, and weak boundaries. A coordinated computerized processing of spine will make the process of diagnosis progressively precise and consistent, further reducing the overall time, effort and cost incurred (Shi et al, 2007).…”