Pixel Gray Level Research Articles

Towards hybrid approach based SVM and Radiomics features for COVID-19 classification and segmentation

In the battle against the COVID-19 pneumonia outbreak, which is brought on by the coronavirus strain SARS-Cov-2, radiological chest exams, such as chest X-rays, are crucial. In order to understand the unique radiographic characteristics of COVID-19, this research looks into classification models to distinguish chest X-ray images based on Radiomics features. This study is performed with datasets composed of 136 segmented chest X-rays, which were used to train and test the categorization algorithms. First and second-order statistical texture characteristics were extracted from the right (R), left (L), superior, middle, and bottom lung zones for each lung side using the Pyradiomics collection. Data was divided into training (80%) and test (20%) groups for feature selection. After assessing the respective feature significance and confirmation accuracy, the most pertinent Radiomics features were chosen. A model of lung segmentation based grey level pixels was used to evaluate support vector machines (SVM) as possible classifiers (AUC = 83.7%). Our research reveals a preference for the upper lung zone and a preponderance of Radiomics feature selection in the right lung. Our future research will concentrate on COVID-19 categorization and segmentation for more precise forecast using a hybrid method based on SVM and Radiogenomics features.

Research on detection method of photovoltaic cell surface dirt based on image processing technology

In view of the reduced power generation efficiency caused by ash or dirt on the surface of photovoltaic panels, and the problems of heavy workload and low efficiency faced by manual detection, this study proposes a method to detect dust or dust on the surface of photovoltaic cells with the help of image processing technology to timely eliminate hidden dangers and improve power generation efficiency.This paper introduces image processing methods based on mathematical morphology, such as image enhancement, image sharpening, image filtering and image closing operation, which makes the image better highlight the target to be recognized. At the same time, it also solves the problem of uneven image binarization caused by uneven illumination in the process of image acquisition. By using the image histogram equalization, the gray level concentration area of the original image is opened or the gray level is evenly distributed, so that the dynamic range of the pixel gray level is increased, so that the image contrast or contrast is increased, the image details are clear, to achieve the purpose of enhancement. When identifying the target area, the method of calculating the proportion of the dirt area to the whole image area is adopted, and the ratio exceeding a certain threshold is judged as a fault. In addition, the improved A* path planning algorithm is adopted in this study, which greatly improves the efficiency of the unmanned aerial vehicle detection of photovoltaic cell dirt, saves time and resources, reduces operation and maintenance costs, and improves the operation and maintenance level of photovoltaic units.

Evaluating the association between radiomic features on breast MRI and dormant tumor cell presence in the SURMOUNT trial.

561 Background: While breast cancer treatment is effective at eradicating primary disease, approximately 20% of patients will ultimately recur at a distant site. Recurrence has been linked to the presence of minimal residual disease (MRD) after treatment, which presents as circulating micrometastases and dormant tumor cells (DTCs) located primarily in the bone marrow. Radiomic features on MRI are imaging biomarkers that have been linked with lesion heterogeneity, which in turn has been associated with disease aggressiveness. Here, we examine the association between radiomic features from the primary lesion on MRI and the presence of post-treatment DTCs. Noninvasively identifying patients at high risk of MRD would facilitate optimized treatment strategies to reduce recurrence risk. Methods: Dynamic contrast-enhanced MRI data from breast cancer survivors enrolled in the SURMOUNT trial (N=104) were retrospectively analyzed. Participants with MRIs collected within 90 days of diagnosis who consented to provide a bone marrow aspirate (BMA) sample were eligible. Working with a board-certified radiologist, we successfully identified and segmented the primary lesion for N=70 patients with BMA outcomes. Using the publicly available CaPTk software, we computed 33 radiomic features for each segmented lesion based on pixel intensity, morphology, and grey level textures. To reduce the dimensionality of the feature space, we used these feature values to group the participants into clusters—termed radiomic phenotypes — via agglomerative hierarchical clustering with significance testing. Fisher’s exact test was applied to test the statistical significance of the association between radiomic phenotype and BMA outcome (± DTC). For all statistical tests, p<0.05 was considered significant. Results: Of the N=70 participants analyzed, 58 were negative and 12 were positive for DTCs present in the bone marrow. Two statistically significant radiomic phenotypes ( p<0.05) were identified that were strongly associated with BMA outcome ( p=0.0251). 11 out of 12 positive outcomes were grouped into the second radiomic phenotype (Table). Conclusions: We found a statistically significant association between DTC presence and radiomic phenotypes derived from MRI data. Given the large percentage of positive outcomes assigned to the second radiomic phenotype, the radiomic phenotypes may indicate low and high risk of MRD, respectively. Importantly, this analysis shows promise for the ability to predict MRD from noninvasive imaging features. These results motivate continued data collection to increase sample size and identify a robust set of radiomic features, which may ultimately be used as predictors in potential models of MRD risk. [Table: see text]

The Efficiency of Gray-Level Ultrasound Histogram Analysis in Patients with Supraspinatus Tendinopathy.

Musculoskeletal ultrasonography is a viable substitute for magnetic resonance imaging (MRI) that offers advantages in terms of time efficiency and cost-effectiveness. The gray-level histogram is a tool used to depict the distribution of pixel gray levels that provide quantitative data. The objective of our research was to establish a threshold value for ultrasonography-measured supraspinatus tendon gray-level values by comparing patients with tendinopathy to those without. This study comprised a cohort of 271 individuals, consisting of 124 patients diagnosed with supraspinatus tendinopathy and 147 cases without the aforementioned condition who underwent shoulder MRI and ultrasound examinations. Two radiologists independently conducted the gray-level histogram analyses. The histogram parameters were determined, including the mean, minimum, median, maximum, fifth, 10th, 25th, 50th, 75th, 90th, and 95th percentiles, as well as skewness, kurtosis, and variance. The interobserver agreement was evaluated using the interclass correlation coefficient. The supraspinatus tendinopathy group's all gray-level values were lower than those of the control group, and the difference was statistically significant (P < .05). The supraspinatus tendinopathy group exhibited greater values of skewness and kurtosis in comparison to the control group (P < .05). The area under the curve of the 95th percentile of the gray-level value was the highest (area under the curve = 0.960; cut-off value = 82.5; sensitivity = 96.7%; specificity = 88.2%). The analysis of the histogram of gray-level values has the potential to be a promising method for the monitoring of patients with supraspinatus tendinopathy. This approach could be considered a feasible alternative to MRI.

An Approach for 3D Modeling of the Regular Relief Surface Topography Formed by a Ball Burnishing Process Using 2D Images and Measured Profilograms.

Advanced in the present paper is an innovative approach for three-dimensional modeling of the regular relief topography formed via a ball burnishing process. The proposed methodology involves capturing a greyscale image of and profile measuring the surface topography in two perpendicular directions using a stylus method. A specially developed algorithm further identifies the best match between the measured profile segment and a row or column from the captured topography image by carrying out a signal correlation assessment based on an appropriate similarity metric. To ensure accurate scaling, the image pixel grey levels are scaled with a factor calculated as being the larger ratio between the ultimate heights of the measured profilograms and the more perfectly matched image row/column. Nine different similarity metrics were tested to determine the best performing model. The developed approach was evaluated for eight distinct types of fully and partially regular reliefs, and the results reveal that the best-scaled 3D topography models are produced for the fully regular reliefs with much greater heights. Following a thorough analysis of the results obtained, at the end of the paper, we draw some conclusions and discuss potential future work.

Fracture risk assessment in diabetes mellitus.

Growing evidence suggests that diabetes mellitus is associated with an increased risk of fracture. Bone intrinsic factors (such as accumulation of glycation end products, low bone turnover, and bone microstructural changes) and extrinsic factors (such as hypoglycemia caused by treatment, diabetes peripheral neuropathy, muscle weakness, visual impairment, and some hypoglycemic agents affecting bone metabolism) probably contribute to damage of bone strength and the increased risk of fragility fracture. Traditionally, bone mineral density (BMD) measured by dual x-ray absorptiometry (DXA) is considered to be the gold standard for assessing osteoporosis. However, it cannot fully capture the changes in bone strength and often underestimates the risk of fracture in diabetes. The fracture risk assessment tool is easy to operate, giving it a certain edge in assessing fracture risk in diabetes. However, some parameters need to be regulated or replaced to improve the sensitivity of the tool. Trabecular bone score, a noninvasive tool, indirectly evaluates bone microstructure by analyzing the texture sparsity of trabecular bone, which is based on the pixel gray level of DXA. Trabecular bone score combined with BMD can effectively improve the prediction ability of fracture risk. Quantitative computed tomography is another noninvasive examination of bone microstructure. High-resolution peripheral quantitative computed tomography can measure volume bone mineral density. Quantitative computed tomography combined with microstructure finite element analysis can evaluate the mechanical properties of bones. Considering the invasive nature, the use of microindentation and histomorphometry is limited in clinical settings. Some studies found that the changes in bone turnover markers in diabetes might be associated with fracture risk, but further studies are needed to confirm this. This review focused on summarizing the current development of these assessment tools in diabetes so as to provide references for clinical practice. Moreover, these tools can reduce the occurrence of fragility fractures in diabetes through early detection and intervention.

An efficient hand gesture recognition based on optimal deep embedded hybrid convolutional neural network‐long short term memory network model

AbstractHand gestures are the nonverbal communication done by individuals who cannot represent their thoughts in form of words. It is mainly used during human‐computer interaction (HCI), deaf and mute people interaction, and other robotic interface applications. Gesture recognition is a field of computer science mainly focused on improving the HCI via touch screens, cameras, and kinetic devices. The state‐of‐art systems mainly used computer vision‐based techniques that utilize both the motion sensor and camera to capture the hand gestures in real‐time and interprets them via the usage of the machine learning algorithms. Conventional machine learning algorithms often suffer from the different complexities present in the visible hand gesture images such as skin color, distance, light, hand direction, position, and background. In this article, an adaptive weighted multi‐scale resolution (AWMSR) network with a deep embedded hybrid convolutional neural network and long short term memory network (hybrid CNN‐LSTM) is proposed for identifying the different hand gesture signs with higher recognition accuracy. The proposed methodology is formulated using three steps: input preprocessing, feature extraction, and classification. To improve the complex visual effects present in the input images, a histogram equalization technique is used which improves the size of the gray level pixel in the image and also their occurrence probability. The multi‐block local binary pattern (MB‐LBP) algorithm is employed for feature extraction which extracts the crucial features present in the image such as hand shape structure feature, curvature feature, and invariant movements. The AWMSR with the deep embedded hybrid CNN–LSTM network is applied in the two‐benchmark datasets namely Jochen Triesch static hand posture and NUS hand posture dataset‐II to detect its stability in identifying different hand gestures. The weight function of the deep embedded CNN‐LSTM architecture is optimized using the puzzle optimization algorithm. The efficiency of the proposed methodology is verified in terms of different performance evaluation metrics such as accuracy, loss, confusion matrix, Intersection over the union, and execution time. The proposed methodology offers recognition accuracy of 97.86% and 98.32% for both datasets.

Image Enhancement Method based on an Improved Fuzzy C-Means Clustering

Image enhancement is an important method in the process of image processing. This paper proposes an image enhancement method base on an improved fuzzy c-means clustering. The method consists of the following steps: firstly, proposed a fuzzy c-means clustering with a cooperation center(FCM-co). Secondly, using the FCM-co, divide the image pixels into different clusters and marked membership values to those clusters. Thirdly, modify the membership values. Finally, calculate the new pixel gray levels. This enhancement method can overcome the disadvantage of overexposure and better retain image details. Through the experiment, the test results show that the proposed enhancement method could achieve better performance.

Success of extracorporeal shock wave lithotripsy based on CT texture analysis.

The aims of the study were to evaluate whether computerised tomography texture analysis (CTTA) based on non-contrast computed tomography (NCCT) has predictive value for the success of extracorporeal-shockwave lithotripsy (ESWL) in upper urinary tract stones (UUTS). This study included 156 of 356 patients undergoing ESWL for UUTS sized 0.5-2cm from 2015 to 2019. Patients with congenital kidney anomalies, radiolucent stones, multiple stones, treated for upper urinary tract stones previously and lower pole stones were excluded from study. The number of ESWL sessions of the patients was as follows: 78 (50%) patients had 1 session, 30 (19.2%) patients had 2 sessions and 48 (30.8%) patients had >2 sessions. First- and second-order CTTA properties of patients' UUTS were evaluated using texture analysis software (LIFEx Software). Other clinical features such as Hounsfield Unit (HU), initial stone size, body-mass index (BMI) and skin to stone distance (SSD) was recorded. The patients were divided into two groups according to ESWL success. Cases with residual stones larger than 4mm were considered failed cases. BMI, the standard deviation of HU, SSD, skewness, kurtosis, entropy and all second-order statistics were found to be statistically different between the two groups except for correlation (P<.05). Multivariate analysis showed longer SSD and four new parameters of CTTA (kurtosis, entropy, dissimilarity and energy by the distribution of pixel grey levels in the UUTS) to be significant predictors for unsuccessful ESWL outcomes. SSD and second-order CTTA properties (dissimilarity and energy) had an area under ROC curve of 0.802, 0.850 and 0.824 at a 95% confidence interval. ESWL success rate in all patients was 76.9%. CTTA can help select patients who will undergo ESWL for upper urinary tract stones. Thus, we can reduce treatment costs and ESWL complications by preventing unnecessary ESWL applications.

Superficial fibromatosis: MRI radiomics and T2 mapping correlate with treatment response

BackgroundSuperficial fibromatosis exhibits variable MR signal intensity due to collagenous and fibroproliferative components. Quantifying this signal heterogeneity using image texture analysis and T2-mapping could have prognostic and therapeutic implications. MethodsThis IRB-approved retrospective study included 13 patients with superficial fibromatosis, managed by observation, electron beam radiotherapy (EBT), or pentoxifylline/vitamin E. Two-dimensional regions of interest (ROIs) were drawn on proton-density or T2-weighted MRI for radiomics feature analysis, and corresponding T2-maps. Comparisons were made between baseline and follow-up T2 relaxation times and radiomics features: Shannon's entropy, kurtosis, skewness, mean of positive pixels (MPP), and uniformity of distribution of positive gray-level pixel values (UPP). ResultsThere were 19 nodules in 13 subjects. Mean patient age was 60 years; 62% (8/13) were female; mean follow-up was 9.7 months. Nodule diameter at baseline averaged 18.2 mm (std dev 16.2 mm) and decreased almost 10% to 16.6 mm (p = 0.1, paired t-test). Normalized T2 signal intensity decreased 23% from 0.71 to 0.55 (p = 0.03, paired t-test). T2 relaxation time decreased 16% from 46.5 to 39.1 ms (p < 0.001, paired t-test). Among radiomics features, skewness increased to 0.71 from 0.41 (p = 0.03, paired t-test), and entropy decreased from 8.37 to 8.03 (p = 0.05, paired t-test); differences in other radiomics features were not significant. ConclusionsRadiomics analysis and T2-mapping of superficial fibromatosis is feasible; robust decreases in absolute T2 relaxation time, and changes in image textural features (increased skewness and decreased entropy) offer novel imaging biomarkers of nodule collagenization and maturation.

Liver Segmentation in MRI Images using an Adaptive Water Flow Model.

Background: Identification and precise localization of the liver surface and its segments are essential for any surgical treatment. An algorithm of accurate liver segmentation simplifies the treatment planning for different types of liver diseases. Although liver segmentation turns researcher’s attention, it still has some challenging problems in computer-aided diagnosis.Objective: This study aimed to extract the potential liver regions by an adaptive water flow model and perform the final segmentation by the classification algorithm.Material and Methods: In this experimental study, an automatic liver segmentation algorithm was introduced. The proposed method designed the image by a transfer function based on the probability distribution function of the liver pixels to enhance the liver area. The enhanced image is then segmented using an adaptive water flow model in which the rainfall process is controlled by the liver location in the training images and the gray levels of pixels. The candidate liver segments are classified by a Multi-Layer Perception (MLP) neural network considering some texture, area, and gray level features.Results: The proposed algorithm efficiently distinguishes the liver region from its surrounding organs, resulting in perfect liver segmentation over 250 Magnetic Resonance Imaging (MRI) test images. The accuracy of 97% was obtained by quantitative evaluation over test images, which revealed the superiority of the proposed algorithm compared to some evaluated algorithms. Conclusion: Liver segmentation using an adaptive water flow algorithm and classifying the segmented area in MRI images yields more robust and reliable results in comparison with the classification of pixels.

Оценка фрактальной размерности поверхности разрушения образцов горных пород

An approach to estimate fractal dimension of a microphotograph of rock sample fracture surface using the Differential Box Counting (DBC) method is presented. The difference between this algorithm and the classical method for fractal dimension calculation is in that the grayscale levels of pixels of analyzed image are taken into account. This approach does not require initial binarization of the image, which is poorly applicable for estimation of fractal dimension of real objects, such as microphotographs of rock sample surface, which in turn does not lead to the loss of useful information. Testing the method on a set of images generated with different content of pixels of gray levels showed that there is a steady increase in fractal dimension values with increase in the number of added pixels. It is shown that fractal dimension for a set of images simulating crack propagation also demonstrates growth with increasing of fracturing. From a geometric point of view, this feature is a consequence of increase of roughness of the surface, which is a set of rectangles, the height of which corresponds to gray level of pixels of analyzed image. This approach can be used to quantify changes in sample surface structure before and after load action, as well as in real time using high-speed photography of the sample surface or radioscopy of internal cracks. Keywords: fractal analysis, fractal dimension, differential box-counting, fracture surface.

Fuzzy inference based contextual dissimilarity histogram equalization algorithm for image enhancement

AbstractIn order to overcome the drawback of the existing image enhancement technologies and further consider the pixel intensity expression error caused by imaging, a novel fuzzy inference‐based contextual dissimilarity histogram equalization (FICDHE) algorithm is proposed. The proposed algorithm is composed of three modules. In the first module, according to the calculated probable intensity intervals, the membership functions of intensity are generated. In the second one, fuzzy inference systems are established and the contextual dissimilarity of each pixel is calculated. In the third module, the contextual dissimilarity histograms are clipped and equalized. The fuzzy system established in this paper not only fully considers the uncertainty source of pixel gray level expression, but also has adaptability. The parameter selection of fuzzy inference system membership function in this algorithm does not need human intervention, but is automatically obtained based on the statistical information of image pixel gray. Its adaptability makes the algorithm more widely used and convenient. Experiments are conducted using four typical medical images and 800 images from the KNIX dataset and BrainWeb dataset. The performance of the proposed method was compared to a series of enhancement algorithms based on both subjective judgment and image quality measurement indexes. Experimental results demonstrate that the proposed algorithm has a better contrast enhancement ability and yields better performance.

Minimum square distance thresholding method applying gray-gradient co-occurrence matrix

In image thresholding segmentation, gray level of pixels is the basic element to describe images. Besides, the gradient information of pixels is also a key feature to represent image space distribution. Therefore, the co-occurrence probability of gray and gradient of pixels is an effective information to describe image. In this paper, gray-gradient asymmetrical co-occurrence matrix is constructed, uniformity probability of image region is produced, and a minimum square distance criterion function based on gray-gradient co-occurrence matrix is proposed to measure the deviation between original and binary images. Comparing with gray-gray asymmetrical co-occurrence matrix and relative entropy-based symmetrical co-occurrence matrix method, the proposed method can obtain more complete segmentation results, especially for small-size object extraction. The peak signal to noise ratio probability also shows the better segmentation performance of our proposed method.

BROCELIANDE: A COMPARATIVE STUDY OF ATTRIBUTE PROFILES AND FEATURE PROFILES FROM DIFFERENT ATTRIBUTES

Abstract. Morphological attribute profiles (APs) are among the most effective spatial-spectral methods to perform multilevel image description based on hierarchical tree-based representation. They have been widely applied to the processing and characterization of remote sensing images, in particular to tackle classification task, in the literature. Recently, a novel extension of APs called FPs has been proposed by replacing pixel gray-levels with some statistical and geometrical features when forming the output profiles. FPs have been proved to be more efficient than the standard APs when generated from both inclusion and partition trees. The motivation of this article is to conduct a comparative study of APs and FPs using different attributes including some novel ones that have not been used in the literature. We also present our developed library called Broceliande, which proposes efficient implementation of APs and FPs to perform remote sensing image classification, with various choices of tree structures as well as attributes. We perform our experiments on two high resolution optical image data sets and provide comparative results of APs and FPs, showing and confirming their effectiveness to describe and classify remote sensing images.

Image encryption based on Kronecker product over finite fields and DNA operation

This paper reports an image encryption algorithm based on a matrix of Kronecker products and a deoxyribonucleic acid (DNA) operation over finite fields. First, a plaintext image is mapped from pixel gray-levels to the finite field. Subsequently, the image pixels are scrambled and diffused simultaneously by the Kronecker-product matrix based on a permutation polynomial over finite fields. Finally, a DNA operation is carried out to obtain greater confusion and scrambling. A widespread of security analyses and experimental results show that the proposed algorithm is safe against several common attacks. Moreover, there were no rounding errors in computation over finite fields, thereby ensuring a strict lossless image encryption and decryption. Due to the intrinsic nonlinearity of the permutation polynomials in the finite field, the proposed image encryption system is nonlinear and can resist known-plaintext and chosen-plaintext attacks.

Deep learning-based method for SEM image segmentation in mineral characterization, an example from Duvernay Shale samples in Western Canada Sedimentary Basin

Texture-based feature extraction and object segmentation are challenging in image processing. In this study, the U-Net architecture developed for biomedical image analysis was used to evaluate geologic characteristics depicted within scanning electron microscope (SEM) images of shale samples. With a revised weight function, the U-Net architecture allowed for effective discrimination of clay aggregates mixed with matrix mineral particles and organic matter (OM). In training, a local variability weight based on spatial statistics was used to enhance the contrast between features across boundary in the loss function of U-Net system optimization, thereby improving the ability of U-Net to distinguish the geologic features specific to our research needs. The Tensorflow neural network library was used to create semantic segmentation and feature extraction models in mineral identification. In the application example of the Devonian Duvernay shale study, we prepared 8000 randomly sliced image cuts (256 × 256 pixels) from four masked image tiles (6144 × 6144 pixels) with tagged feature objects, among which 6400 are for training and the remaining 1600 held image slices for validation. In the validation, the average of intersection over union (IOU) reaches 91.7%. The trained model approved by validation was used for clay aggregate segmentation and mineral classification. Three hundred SEM image tiles of source rock samples from different maturities in the Duvernay Formation were processed using the proposed workflow. The results show that the clay aggregates are clearly separated from other matrix mineral particles with acceptable boundaries, although both exhibit indistinguishable grey-level pixels. This approach demonstrates that texture-based deep learning feature extraction is feasible, cost-effective and timely, and can help geoscientists gain new insights by quantitatively analyzing specific geological characteristics and features.

Image encryption based on permutation polynomials over finite fields

In this paper, we propose an image encryption algorithm based on a permutation polynomial over finite fields proposed by the authors. The proposed image encryption process consists of four stages: i) a mapping from pixel gray-levels into finite field, ii) a pre-scrambling of pixels’ positions based on the parameterized permutation polynomial, iii) a symmetric matrix transform over finite fields which completes the operation of diffusion and, iv) a post-scrambling based on the permutation polynomial with different parameters. The parameters used for the polynomial parameterization and for constructing the symmetric matrix are used as cipher keys. Theoretical analysis and simulation demonstrate that the proposed image encryption scheme is feasible with a high efficiency and a strong ability of resisting various common attacks. In addition, there are not any round-off errors in computation over finite fields, thus guaranteeing a strictly lossless image encryption. Due to the intrinsic nonlinearity of permutation polynomials in finite fields, the proposed image encryption system is nonlinear and can resist known-plaintext and chosen-plaintext attacks.

An improved Otsu threshold segmentation algorithm

Image segmentation is widely used as a fundamental step for various image processing applications. This paper focuses on improving the famous image thresholding method named Otsu's algorithm. Based on the fact that threshold acquired by Otsu's algorithm tends to be closer to the class with larger intraclass variance when the foreground and background have large intraclass variance difference, an improved strategy is proposed to adjust the threshold bias. We analysed the relationship between pixel greyscale value and the change of cumulative pixel number, and selected the ratio of pixel grey level value to a certain cumulative pixel number as the adjusted threshold. Experiments using typical testing images were set up to verify the proposed method both quantitatively and qualitatively. Two widely used metrics named misclassification error (ME) and dice similarity coefficient (DSC) were adopted for quantitative evaluation, and both quantitative and qualitative results indicated that the proposed algorithm could better segment the testing images and get competitive misclassification error and DSC values compared with Otsu's method and its improved versions proposed by Hu and Gong (2009) and Xu et al. (2011), and the time consumption of our method can be significantly reduced.

Semi-automatic unsupervised MR brain tumour segmentation using a simple Bayesian Framework

ABSTRACTTumours, one of the pathologies of the brain, are diverse in shape and appearance and overlap with the normal brain tissues making accurate automatic segmentation a challenge. This work proposes a semi- automatic, unsupervised method for brain tumour segmentation, using magnetic resonance images in a simple Bayesian framework. Pixel is classified as a tumour class by taking into account the knowledge of different brain tissue classes, grey level of pixels and their neighbourhood. For the Bayesian frame work, the likelihood of the different brain tissue classes is assumed as Gaussian and Gaussian density weights of the pixel neighbourhood serve as the prior information for accurate tumour segmentation. Experiments conducted on the publically available BRATS database result in an overall accuracy of 98% for tumour core and 96% for oedema.