TITLE:
Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach
AUTHORS:
Amira S. Ashour, Sourav Samanta, Nilanjan Dey, Noreen Kausar, Wahiba Ben Abdessalemkaraa, Aboul Ella Hassanien
KEYWORDS:
Meta-Heuristic, Cuckoo Search, Image Enhancement, Medical Imaging, Log Transform
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.6 No.3,
August
31,
2015
ABSTRACT: Medical image enhancement is an essential
process for superior disease diagnosis as well as for detection of pathological
lesion accurately. Computed Tomography (CT) is considered a vital medical
imaging modality to evaluate numerous diseases such as tumors and vascular
lesions. However, speckle noise corrupts the CT images and makes the clinical
data analysis ambiguous. Therefore, for accurate diagnosis, medical image
enhancement is a must for noise removal and sharp/clear images. In this work, a
medical image enhancement algorithm has been proposed using log transform in an
optimization framework. In order to achieve optimization, a well-known
meta-heuristic algorithm, namely: Cuckoo search (CS) algorithm is used to
determine the optimal parameter settings for log transform. The performance of
the proposed technique is studied on a low contrast CT image dataset. Besides
this, the results clearly show that the CS based approach has superior
convergence and fitness values compared to PSO as the CS converge faster that
proves the efficacy of the CS based technique. Finally, Image Quality Analysis
(IQA) justifies the robustness of the proposed enhancement technique.