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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
M. Usman Akram
Bahria University
Computer Science & IT

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Blood Vessel Enhancement and Segmentation for Screening of Diabetic Retinopathy M. Usman Akram; Ibaa Jamal; Anam Tariq
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 2: June 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i2.802

Abstract

Diabetic retinopathy is an eye disease caused by the increase of insulin in blood and it is one of the main cuases of blindness in idusterlized countries. It is a progressive disease and needs an early detection and treatment. Vascular pattern of human retina helps the ophthalmologists in automated screening and diagnosis of diabetic retinopathy. In this article, we present a method for vascular pattern ehnacement and segmentation. We present an automated system which uses wavelets to enhance the vascular pattern and then it applies a piecewise threshold probing and adaptive thresholding for vessel localization and segmentation respectively. The method is evaluated and tested using publicly available retinal databases and we further compare our method with already proposed techniques. 
Lung Nodule Detection in CT Images using Neuro Fuzzy Classifier Anam Tariq; M. Usman Akram
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 2: June 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i2.934

Abstract

Automated lung cancer detection using computer aided diagnosis (CAD) is an important area in clinical applications. As the manual nodule detection is very time consuming and costly so computerized systems can be helpful for this purpose. In this paper, we propose a computerized system for lung nodule detection in CT scan images. The automated system consists of two stages i.e. lung segmentation and enhancement, feature extraction and classification. The segmentation process will result in separating lung tissue from rest of the image, and only the lung tissues under examination are considered as candidate regions for detecting malignant nodules in lung portion. A feature vector for possible abnormal regions is calculated and regions are classified using neuro fuzzy classifier. It is a fully automatic system that does not require any manual intervention and experimental results show the validity of our system.
Co-Authors Anam Tariq Anam Tariq Ibaa Jamal