Atherosclerosis may cause progressive cerebral ischemia or cerebral vascular insufficiency; it may lead to progression of mental disorder even to stroke, motor impairment, and cognitive disability and even to death [1]. Due to stroke the annual rate of mortality in US is about 800000. Mostly older people are affected by this disease. People with high blood pressure, high cholesterol and smoking habit are more likely to develop atherosclerosis. Signs and symptoms include numbness or weakness of arms or legs, difficult in speech, temporary loss of vision [2]. The most perfused organ in human body is brain, and in human body blood is first supplied to brain, hence the brain tissues are very sensitive to decrease in blood supply. It has been believed over decades that atherosclerosis lesion in major extra and intra cranial branches may cause ischemic disorders. Around 88% of ischemic disorder is due to Atherosclerosis. The progression of cerebrovascular disease is often a sufficiently slow process. Since it spreads widely many arterial branches are affected. Neurodegenerative disorder leads to long chronic ischemia which is slowly progressing that may lead to cognitive impairment, atrophic tissue changes and even mental disorder if treatment for such disorders is not given at early stage of the disease. For an aggravating atherosclerosis lesion a reconstructive transcatheter surgery is required. According to the severity of the disease, treatment process varies. Low risk patients are treated with medications and high-risk patients are recommended for surgical treatment. The poor blood circulation process in blood vessels of human brain leads to the formation of abnormal blood vessels. A mass of semi-solid blood forms the blood clot. In absence of blood clot blood flow is normal and move freely through arteries and veins. Based upon the type of blood clot and its location it is necessary to evaluate the coagulation or blood clot. [1] Majorly two types of brain stroke occur. One is hemorrhage stoke and the other is ischemic stroke. Hypertension, hyperlipidemia, diabetes, micro-emboli caused by carotid arrhythmias plays an important role in its progression.

The quality of image plays a very important role in medical image processing. Based on accuracy of segmentation the quality of medical image diagnosis will be successful. Image quality that provides human body’s unparalleled view is one of the most important factors for identification of atherosclerosis. [3]

In order to detect Cerebral Atherosclerosis, image processing steps like Preprocessing and Segmentation is required. Preprocessing is a technique used to enhance the images and also it removes noise from the image. Different methods like image scaling, Gray scale conversion is used followed by Alpha trimmed mean filter which is used to remove noise. Image normalization techniques like histogram stretching, histogram equalization is performed to increase the contrast of image and Image Sharpening is performed over image to highlight fine details and edges in an image [4]. Image segmentation is used to split the image into meaningful region. It is used to provide distinction between the region we want to inspect further with region in the background. The previous techniques for analyzing brain atherosclerosis are unable to produce better and accurate identification of affected lesion area. The proposed method analyses and segments atherosclerotic lesion area by forming a hybrid algorithm that combines the pros of three major clustering algorithms such as K-means, Contextual Clustering and Particle Swarm Optimization. Proposed method is depicted in flow diagram Fig. 1.

Santichai Fueanggan et al. (2011) [5] proposed a methodology to specify area affected by Ischemic stroke by dividing image data with statistical method and probability into two different areas, they are normal and abnormal areas in the brain. They assign threshold level automatically in CTP, CBV and CBF images. Images are finally divided to non-tissue area, dead tissue area and mismatch area. Finally concluded with brain by separating ischemic stroke area and the results are compared with brain specialist’s suggestion.

Liao et al. (2016) [6] proposed a method for detection and classification of hematoma and type of it using decision rules. Multi resolution Thresholding and region growing methods were used for identification of hematoma region.

Spyretta Golemati et al. (2013) [7] analyzed ultrasound images of carotid a low-cost imaging system. Valuable information such as plaque composition and its stability revealed by Ultrasound images are considered to be the basic information. And further analysis made on atherosclerosis and risk towards Ischemic stroke.

Gharnali et al. (2018) [8] proposed an effective method for segmentation of Magnetic resonance images using FCM technique. FCM an effective object function has been constructed in this technique by replacing Euclidean distance with kernel induced distance. Experiments were performed on synthetic images as well as on MRI images qualitatively and quantitatively using Dice factor for segmentation.

Eero Salli et al. (2001) [9] presented contextual clustering algorithm for calculating SPM (Statistical Parametric maps) from three dimensional image. Neural activations from fMRI images are detected. This divides activation and non-activation area that helps to detect false activation. The system uses a Markov random field prior and Iterated Conditional Modes (ICM) algorithm for the above process.

Chawla et al. (2009) [10] proposed a technique for detection and classification of all types of stroke like acute, chronic and hemorrhagic present in brain images. The abnormalities were detected using two-level classification scheme. Histogram features are used in first level followed by wavelet-based features in second level.

Tang FH (2011) [11] proposed a novel Circular Adaptive Region of Interest (CAROI) method to analyze the Computed Tomography (CT) images of the brain. It is used detect ischemic stroke with small lesions using image feature characteristics in early manner and it is shown that improvement in sensitivity and specificity are achieved. Also, it helps to improve the efficiency and accuracy of clinical practice.

For desired segmentation, a single segmentation method cannot achieve good result usually, this is due to medical image complexity. Adoption of combination of different approach is necessary for better segmentation process [12]. For segmenting the image in a most efficient manner a novel algorithm is formed by combining multiple algorithms together. The Hybrid algorithm includes K-means algorithm, Contextual clustering and Particle Swarm Optimization (PSO) [13].

Data set consists of various MRI images and it is collected from Madras Scans and research gate and are preprocessed using alpha trimmed mean filter. Input image of patient MSA9 is shown in Fig. 3a, Resized image is shown in Fig. 3b, Denoised image is shown in Fig. 3c, Skull stripped image is shown in Fig. 3d, Morphological opening image is shown in Fig. 3e, Segmentation with multiple iteration image is shown in Fig. 3f, Region of interest result after PSO is shown in Fig. 3g and Affected region is shown in Fig. 3h.

The parameters like Tumor area, Pixel count and Correlation of Abnormal patient is taken into account and comparison chart of these parameters using K-Means, Contextual Clustering and Proposed Hybrid method is shown in Figs. 5a–5c and from the chart we could able to analyze tumor area, pixel count and correlation parameter is good in proposed hybrid method.

The parameters like Tumor area, Pixel count and Correlation of Normal Patient is taken into account and comparison chart of these parameters using K-Means, Contextual Clustering and Proposed Hybrid method is shown in Figs. 6a–6c and from the chart we could able to analyze tumor area, pixel count is zero for proposed method and non-zero for other two methods also correlation parameter is high in proposed hybrid method.

For undertaking the brain surgery, detection of affected region plays a major role in brain images. Different algorithms for atherosclerosis lesion removal have been investigated and its detection through various segmentation algorithms is carried out. The pinpoint of the exact location of the lesion can be provided by MRI scan. Based on steps followed on each algorithm the different outputs are obtained. The lesion recognized is evaluated by a performance measures like tumor area, pixel count and correlation. Segmentation of affected brain area with Hybrid combination of K-Means, Contextual clustering and PSO has been performed with successful results in various abnormal MRI images. This novel hybrid algorithm gives better result when compared to existing algorithms like K-means and contextual clustering method in detection of the atherosclerotic lesion.