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Amalgamation of iterative double automated thresholding and morphological filtering: a new proposition in the early detection of cerebral aneurysm

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Abstract

Cerebral aneurysm (CA) has been emerging as one of the life threatening diseases in adults which results due to the pathological distension of cerebral arteries. Rupture of cerebral aneurysms causes subarachnoid hemorrhage (SAH) which is having a miserable prognosis. SAH is one of the cerebrovascular diseases with the highest mortality. With the rapid improvement in the field of medical image processing, prior detection of cerebral (intracranial) aneurysms before rupture is on a high rise. In this communication, we have made one novel attempt to detect CA from medical images through efficient amalgamation of automated thresholding and morphological filtering. In regard to this, an iterative double automated thresholding (IDAT) algorithm has been proposed which exhibits superiority over other existing thresholding techniques like Sauvola, Niblack and Otsu’s threshold. Efficiency of the proposed algorithm has been validated over a number of digital subtraction angiography (DSA) images in terms of accuracy, sensitivity and specificity. The performance of the proposed method has also been compared with other existing methods for CA detection and finally its supremacy has been substantiated.

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Authors and Affiliations

  1. Department of Instrumentation & Electronics Engineering, Jadavpur University, Sector III, Block LB, Plot No. 8, Salt Lake City, Salt Lake Bypass, Kolkata, West Bengal, 700098, India

    Abhijit Chandra

  2. School of Medical Science & Technology, Indian Institute of Technology, Kharagpur, India

    Sumita Mondal

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  2. Sumita Mondal
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Correspondence to Abhijit Chandra.

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Chandra, A., Mondal, S. Amalgamation of iterative double automated thresholding and morphological filtering: a new proposition in the early detection of cerebral aneurysm. Multimed Tools Appl 76, 23957–23979 (2017). https://doi.org/10.1007/s11042-016-4149-9

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