Abstract：
:Precise segmentation of stroke lesions from brain magnetic resonance (MR) images poses a challenging task in automated diagnosis. In this paper, we proposed a new method called watershed-based lesion segmentation algorithm (WLSA), which is a novel intensity-based segmentation technique used to delineate infarct lesion in diffusion-weighted imaging (DWI) MR images of the brain. The algorithm was tested on a series of 142 real-time images collected from different stroke patients reported at IMS and SUM Hospital. One MRI slice having largest area of infract lesion is selected from each patient from multiple slices. The main objective is to combine the strength of guided filter and watershed transform through relative fuzzy connectedness (RFC) to detect lesion boundaries appropriately. The extracted informative statistical and geometrical features are used to classify the types of stroke lesions according to the Oxfordshire Community Stroke Project (OCSP) classification. The experimental results demonstrated the effectiveness of the proposed process with high accuracy in delineating lesions. A classification with a dice similarity index (DSI) of 96% with computational time of 0.06 s in random forest (RF) and an accuracy of 85% with computational time of 0.84 s has been obtained by multilayer perceptron (MLP) neural network classifier in tenfold cross-validation process. Better detection accuracy is achieved in RF classifier in classifying stroke lesions.
journal_name
Med Biol Eng Computjournal_title
Medical & biological engineering & computingauthors
Subudhi A,Jena S,Sabut Sdoi
10.1007/s11517-017-1726-7subject
Has Abstractpub_date
2018-05-01 00:00:00pages
795-807issue
5eissn
0140-0118issn
1741-0444pii
10.1007/s11517-017-1726-7journal_volume
56pub_type
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