Hybrid Segmentation Method Research Articles

An open source auto-segmentation algorithm for delineating heart and substructures – Development and validation within a multicenter lung cancer cohort

Background and purposeIrradiation of the heart in thoracic cancers raises toxicity concerns. For accurate dose estimation, automated heart and substructure segmentation is potentially useful. In this study, a hybrid automatic segmentation is developed. The accuracy of delineation and dose predictions were evaluated, testing the method's potential within heart toxicity studies. Materials and MethodsThe hybrid segmentation method delineated the heart, four chambers, three large vessels, and the coronary arteries. The method consisted of a nnU-net heart segmentation and partly atlas- and model-based segmentation of the substructures. The nnU-net training and atlas segmentation was based on lung cancer patients and was validated against a national consensus dataset of 12 patients with breast cancer. The accuracy of dose predictions between manual and auto-segmented heart and substructures was evaluated by transferring the dose distribution of 240 previously treated lung cancer patients to the consensus data set. ResultsThe hybrid auto-segmentation method performed well with a heart dice similarity coefficient (DSC) of 0.95, with no statistically significant difference between the automatic and manual delineations. The DSC for the chambers varied from 0.78-0.86 for the automatic segmentation and was comparable with the inter-observer variability. Most importantly, the automatic segmentation was as precise as the clinical experts in predicting the dose distribution to the heart and all substructures. ConclusionThe hybrid segmentation method performed well in delineating the heart and substructures. The prediction of dose by the automatic segmentation was aligned with the manual delineations, enabling measurement of heart and substructure dose in large cohorts. The delineation algorithm will be available for download.

Automated methods for indoor point cloud preprocessing: Coordinate frame reorientation and building exterior removal

Raw indoor datasets acquired from laser scanning may be tilted and contain outliers, which negatively influence the accuracy of subsequent tasks. Therefore, an automatic framework with three steps is provided to preprocess indoor point cloud. First, a hybrid segmentation method is proposed to extract planar primitives from noisy points. Then, a nonlinear optimization is designed to discover the dominant orientations of the building with topological constraints. Finally, the outdoor points are removed through a novel graph-cut formulation. The experiments on six various buildings indicates that our methods successfully reorient the point cloud and preserve the indoor points. In comparison with other methods, we achieve superior performance on visual inspection and evaluation metrics. Besides, our work only relies on the geometric position and is not limited by Manhattan-World assumption. We present a reliable solution for indoor point cloud preprocessing that have potential for indoor modelling, floorplan generation, and objects detection.

Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring.

Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing techniques to help with diagnosing and monitoring PCOS. This study proposes a combination of Otsu's thresholding with the Chan-Vese method to segment and identify follicles in the ovary with reference to ultrasound images marked by a medical practitioner. Otsu's thresholding highlights the pixel intensities of the image and creates a binary mask for use with the Chan-Vese method to define the boundary of the follicles. The acquired results were compared between the classical Chan-Vese method and the proposed method. The performances of the methods were evaluated in terms of accuracy, Dice score, Jaccard index and sensitivity. In overall segmentation evaluation, the proposed method showed superior results compared to the classical Chan-Vese method. Among the calculated evaluation metrics, the sensitivity of the proposed method was superior, with an average of 0.74 ± 0.12. Meanwhile, the average sensitivity for the classical Chan-Vese method was 0.54 ± 0.14, which is 20.03% lower than the sensitivity of the proposed method. Moreover, the proposed method showed significantly improved Dice score (p = 0.011), Jaccard index (p = 0.008) and sensitivity (p = 0.0001). This study showed that the combination of Otsu's thresholding and the Chan-Vese method enhanced the segmentation of ultrasound images.

A Novel Approach for Hybrid Image Segmentation GCPSO: FCM Techniques for MRI Brain Tumour Identification and Classification.

In recent times, the early detection of brain tumour analysis and classification has become a very vital part of the medical field. The MRI scan image is the most significant tool to study brain tissue for proper diagnosis and efficient treatment planning to detect the early stages. In this research study, the two contributions were executed in the preprocessing mode. (a) Using wavelet transform to apply decomposed sub-bands of a low-frequency signal to control and adapt the spatial and intensity parameters in a bilateral filter and (b) to detect texture regions and block boundary to control and adapt the spatial and intensity parameters in a bilateral filter When compared to other image resolution methods, the adaptive bilateral method restores the original image quality and has a higher accuracy rate. Using the hybrid segmentation method of GCPSO (Guaranteed Convergence Particle Swarm Optimization) -FCM (Fuzzy C-Mean) techniques, the results were compared with various segmentation. The proposed segmentation gives a better accuracy rate of 95.32%.

A hybrid image segmentation method for building extraction from high-resolution RGB images

Buildings are the most striking artificial features, and extracting buildings becomes critical for many service-providing agencies. Although improvements have been achieved, building detection from remotely sensed images is still challenging. In the past few years, many building extraction methods have been put forward by researchers, such as line- or edge-based, template matching, knowledge- and auxiliary data-based, machine learning, morphological operations-based, and GEographic Object-Based Image Analysis (GEOBIA) -based. GEOBIA is a paradigm for analyzing high-resolution images; however, GEOBIA-based building extraction methods encounter problems in the segmentation and classification stage. Thus, the accuracy of those methods is lesser than other building detection approaches. This research introduced several modifications to the previously proposed hybrid segmentation methods, such as using the reference polygon to identify optimal parameters, a donut filling technique to reduce over-segmentation caused by roof elements, and illumination differences to restrict merging with shadow. The proposed methodology was tested on a UAV image with visible bands only. Better results were achieved using this approach when compared to the multiresolution proposed by Baatz and Schäpe (2000) and the other two-hybrid methods proposed by Wang et al. (2018a) and Yang et al. (2017). This hybrid segmentation method was also applied to subsets of the Wuhan University buildings dataset and produced similar results. One of the great strengths of the proposed method was that there were no parameter tuning and user interaction at running time. In addition, it was able to segment both small and large buildings without using any scale or object size parameters.

Sickle cell segmentation and classification for thalassemia aid diagnosis

Background: Thalassemia is a hereditary blood disease in which abnormal red blood cells (RBCs) carry insufficient oxygen throughout the body. Conventional methods of thalassemia detection through a complete blood count (CBC) test and peripheral blood smear image still possess a lot of weaknesses. Methods: This paper proposes a hybrid segmentation method to segment the RBCs. It incorporates adaptive thresholding and canny edge method to segment the RBCs. Morphological operations are performed to clean the leftovers. Shape and texture features are extracted using the segmented masks and the gray level co-occurrence matrix. Data imbalance treatment is used for solving the imbalance cell type class in distribution. In the data resampling layer, the synthetic minority oversampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and random over sampling (ROS) are performed and evaluated using the decision tree and logistic regression. In the classification layer, the decision tree, random forest classifier and support vector machine (SVM) are assessed and compared for the best performance in classification. Results:The proposed method outperforms the other methods in the image segmentation layer with the structural similarity index measure (SSIM) of 89.88%. In the data resampling layer, ADASYN is employed as it is more accurate than the SMOTE and ROS. The random forest classifier is chosen at the classification layer as it is more accurate than the decision tree and support vector machine (SVM). Conclusions:The proposed method is tested on the latest dataset of erythrocyteIDB3 and it solves the issues of imbalanced data due to the insufficient cell classes.

Automated Computer Aided Diagnosis Using Altered Multi-Phase Level Sets in Application to Categorize the Breast Cancer Biopsy Images

As macroscopic Breast Cancer (BC) imaging diagnostics do not provide satisfied data, microscopic image analysis is used to get optimum cell-nuclei segmentation for image categorization. The paper presents a Novel Hybrid Segmentation Method which uses the energy detail coefficients attained from Non-Subsampled Contourlet Transform for cell-nuclei segmentation of BC histopathological images. NHSM solves the local inhomogeneities problem while preserving all necessary details. The system evaluation is done using training dataset of 98 BC histopathological images and testing dataset of 26 images. Total of 25 segmented features was extracted for classification using Decision Trees, k-Nearest Neighbor, multilayer perceptron, Multiclass-Support Vector Machine (M-SVM). The proposed NHSM segmentation results were undergone through both subjective and objective performance evaluation. Result evaluation proved that NHSM along with Multi Class-Support Vector Machine classifier provides highly precise and accurate classified result for BC tissue images compared to state-of-the-art segmentation methods. Also, the edge detection model initialization is highly robust and insensitive.

USING A NEW HYBRID SEGMENTATION TECHNIQUE TO SEGMENT LUNAR CRATERS AND DETERMINE ITS DIAMETER

Segmentation method is the process of partition digital image into parts depending to color, texture, and intensity. There are many segmentation methods used in different fields according to the purpose of application. In this study the global thresholding and proposed hybrid method were used to segment lunar craters. Craters on Moon's surface caused by collision between Moon and celestial objects as comet, meteorite, asteroids and others. Due to the Moon has no atmosphere, the lunar surface covered by a huge number of craters different in their size and depth depending on velocity and size of collided objects. The study of lunar craters provide information about the age and geology of a Moon's surface. So, we proposed a novel hybrid segmentation method to segment Moon's images and isolate lunar craters from other surface features and then determine the diameter of lunar craters. The proposed hybrid method combine the performance of K-Means and SFFNN together. The results shown that, the proposed method gives very acceptable outcome, where the boundaries of lunar craters were delineate in professional way that lead to accurate determination of its diameters.

Adaptive thresholding technique based classification of red blood cell and sickle cell using Naïve Bayes Classifier and K-nearest neighbor classifier

Detection of anomalous cells by analyzing the microscopic blood smear plays a key role in identification of various blood diseases in medical field. It becomes easier with robust segmentation steps followed by a classifier for automatic recognition of normal as well as anomalous blood cells. Sickle cell anemia is a disease that changes shape of the red blood cell (RBC) into sickle cell which can be detected by use of automatic classifier. In this reported work, a novel robust hybrid segmentation method is introduced for segmenting normal RBCs and sickle cells. The hybrid segmentation process proposed here is a combination of two segmentation methods namely fuzzy C-means and adaptive (local) thresholding. Four different adaptive thresholding methods are experimented here in combination with fuzzy C-means. The features extracted for carrying out this analysis are major axis, minor axis, aspect ratio, area, perimeter, form factor (metric value), eccentricity and solidity of each of the cells present in the sample blood smear. These eight features are used for training and testing the classifiers. For classification, two supervised classifiers, i.e. Naïve Bayes classifier and K-nearest neighbor classifier have been demonstrated on a dataset of 10 image data samples and the evaluated results are compared for each of the hybrid combinations. The comparison shows that the proposed hybrid combination of fuzzy C-means and NICK's thresholding alongwith K-nearest neighbor classifier gives the best performance in terms of the considered performance parameters. This combination gives a significant classification accuracy of 98.87% with lowest computational time.

A new effective hybrid segmentation method based on C–V and LGDF

Image segmentation is a significant research topic in image processing and computer vision. Active contour methods (ACMs) are widely used in image segmentation. In this paper, a new hybrid ACM segmentation model based on Chan–Vese (C–V) and Local Gaussian Distribution Fitting (LGDF) methods is proposed for the images with intensity inhomogeneity. In this model, new gradient descent flow equations are proposed and applied for the energy minimization of C–V and LGDF methods. Firstly, the proposed C–V method is applied to the image to effectively and quickly find the homogeneous regions of the image. Then, the proposed LGDF method is performed in these regions to detect inhomogeneous areas of the image. Thus, more effective and successful segmentation is obtained for inhomogeneous images. Experimental results show that the satisfactory segmentation results have been obtained by the proposed method for MRI and real images. Also, the proposed method is compared with the local binary fitting, LGDF, adaptive local-fitting-based, global and local weighted signed pressure ACMs, and convolutional neural network-based methods.

Accurate Liver Border Identification Model in CT Scan Images

The liver is an important organ in human body with certain variations in its edges, color, shape and pixel intensity distribution. These uncertainties may be because of various liver pathologies, hereditary or both. Along with it, liver has close proximity to its nearby organs. Hence, identifying liver in scanned images is a challenging step in image processing. This task becomes more imprecise when liver diseases are present at the edges. The liver segmentation is prerequisite for liver volumetry, computer-based surgery planning, liver surgery modelling, surgery training, 3D view generation, etc. The proposed hybrid segmentation method overcomes the problems and identifies liver boundary in Computed-Tomography (CT) scan images accurately. In this paper, the first step is to study statistics of pixel intensity distribution within liver image, and novel methodology is designed to obtain thresholds. Then, threshold-based segmentation is applied which separates the liver from abdominal CT scan images. In the second step, liver edge is corrected using improved chain code and Bresenham pixel interconnection methods. This provides a precise liver image. The initial points are located inside the liver region without user interventions. These initial points evolve outwardly using Fast Marching Method (FMM), identifying the liver boundary accurately in CT abdominal scan images.

Right Ventricular Mass Quantification Using Cardiac CT and a Semiautomatic Three-Dimensional Hybrid Segmentation Approach: A Pilot Study.

ObjectiveTo evaluate the technical applicability of a semiautomatic three-dimensional (3D) hybrid CT segmentation method for the quantification of right ventricular mass in patients with cardiovascular disease.Materials and MethodsCardiac CT (270 cardiac phases) was used to quantify right ventricular mass using a semiautomatic 3D hybrid segmentation method in 195 patients with cardiovascular disease. Data from 270 cardiac phases were divided into subgroups based on the extent of the segmentation error (no error; ≤ 10% error; > 10% error [technical failure]), defined as discontinuous areas in the right ventricular myocardium. The reproducibility of the right ventricular mass quantification was assessed. In patients with no error or < 10% error, the right ventricular mass was compared and correlated between paired end-systolic and end-diastolic data. The error rate and right ventricular mass were compared based on right ventricular hypertrophy groups.ResultsThe quantification of right ventricular mass was technically applicable in 96.3% (260/270) of CT data, with no error in 54.4% (147/270) and ≤ 10% error in 41.9% (113/270) of cases. Technical failure was observed in 3.7% (10/270) of cases. The reproducibility of the quantification was high (intraclass correlation coefficient = 0.999, p < 0.001). The indexed mass was significantly greater at end-systole than at end-diastole (45.9 ± 22.1 g/m2 vs. 39.7 ± 20.2 g/m2, p < 0.001), and paired values were highly correlated (r = 0.96, p < 0.001). Fewer errors were observed in severe right ventricular hypertrophy and at the end-systolic phase. The indexed right ventricular mass was significantly higher in severe right ventricular hypertrophy (p < 0.02), except in the comparison of the end-diastolic data between no hypertrophy and mild hypertrophy groups (p > 0.1).ConclusionCT quantification of right ventricular mass using a semiautomatic 3D hybrid segmentation is technically applicable with high reproducibility in most patients with cardiovascular disease.

Point Cloud Simplification Algorithm for Oral Cavity Model based on Feature Significance Evaluation

Point cloud simplification is an important pretreatment method for the surface reconstruction of oral cavity model with complex surface features and point cloud uneven sampling. The difficulty lies in how to accurately extract feature points and efficiently simplify redundant points to meet the requirements of high-precision reconstruction of oral cavity model. In this paper, we proposed a point cloud simplification algorithm for oral cavity model based on feature significance evaluation. First, point cloud down sampling is used to simplify the original points and k-neighboring points are searched by dynamic search cube method. Second, the feature significance evaluation formula is constructed by using feature measurement factors such as curvature, average projection distance and average spatial distance to extract the feature points. Finally, a hybrid segmentation method is used to segment remaining points, and the simplification rules and octree structure are used to realize the non-uniform simplification of the non-feature points. The experimental results show that the proposed algorithm can well retain the detail features of complex surfaces such as tooth contour and tooth structure, avoid the occurrence of holes in flat areas and the expansion of interdental holes. The algorithm has achieved good effects in terms of simplicity and accuracy.

Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

Segmentation methods can mutually exclude the location of the tumor. However, the challenge of complex location or incomplete identification is located in segmentation challenge dataset. Identificationof tumor location is difficult due to the variation of intensities in MRI image. Vairation of intensity extends up to edema. Confidence Region with Contour Detection identifies the variation of intensities and level set algorithm (Region Scale Fitting) is used to delineate among the region of inner and outer of the tumor. Automatic feature selection method is required due to data complexity. An improved Self Organization Feature Map. Method is required. Weighted SOM Map selects a deterministic feature. This feature is one higher trained accuracy feature. When this specific feature is combines with cluster therefore it is known as deterministic feature clustering. This method gives confidence element. Confidence Region with Contour detection is facing the issue due to extended variations of intensities. These intensities are segmented by hybrid SOM Pixel Labelling with Reduce Cluster Membership and Deterministic Feature Clustering. This hyhbrid method segments the complex tumor intensities. This method produces a potential cluster which is achieved through the hybrid of three unsupervised learning techniques. Hybrid cluster method segments the tumor region. Extended intensities are also segmented by this hybrid approach. Above methods are validated on MICCAI BraTs brain tumor dataset, this is a segmentation challenge dataset. Proposed hybrid algorithm is efficient and it's accuracy can be seen with testing parameters like Dice Overlap Index, Jaccard Tanimoto Coefficient Index, Mean Squared Error and Peak Signal to Noise Ratio. Dice OverlapIndex is 98%, Jaccard Index is 96 percent, Mean Squared Error is 0.06 and Peak Signal To Noise ratio is 18db. The performance of the suggested algorithm is compared to other state of the art.

Hybrid image segmentation method based on anisotropic Gaussian kernels and adjacent graph region merging.

Image segmentation is a key technique in image analysis for object identification. In this paper, a hybrid segmentation method is proposed, which is based on the Anisotropic Gaussian Kernel (ANGK) edge detector and region adjacent graph (RAG) merging algorithm. An anisotropic directional derivative filter is constructed by angled ANGK to detect the edge contour of original images. Based on the gradient magnitude pattern of the edge contour from ANGK processing, watershed transform is adopted to produce initial partition (coarse segmentation result). Finally, combined with the RAG region merging algorithm, the proposed method performs fine segmentation by merging similar fragmented regions (initial partition) iteratively. Additionally, statistic similarity measure and shape cost function in merging cost are also introduced. They provide quantitative criteria for region merging, which have critical influences on the detection result. A series of experiments are conducted to evaluate the performance of this method, and a preferable localization accuracy as well as noise robustness is proved. Compared with conventional edge and region based methods, the proposed one has a superior segmentation effect. Another advantage is that the problem of oversegmentation can be solved effectively.

Developing a Semantic-Driven Hybrid Segmentation Method for Point Clouds of 3D Shapes

With the rapid development of point cloud processing technologies and the availability of a wide range of 3D capturing devices, a geometric object from the real world can be directly represented digitally as a dense and fine point cloud. Decomposing a 3D shape represented in point cloud into meaningful parts has very important practical implications in the fields of computer graphics, virtual reality and mixed reality. In this paper, a semantic-driven automated hybrid segmentation method is proposed for 3D point cloud shapes. Our method consists of three stages: semantic clustering, variational merging, and region remerging. In the first stage, a new feature of point cloud, called Local Concave-Convex Histogram, is introduced to first extract saddle regions complying with the semantic boundary feature. All other types of regions are then aggregated according to this extracted feature. This stage often leads to multiple over-segmentation convex regions, which are then remerged by a variational method established based on the narrow-band theory. Finally, in order to recombine the regions with the approximate shapes, order relation is introduced to improve the weighting forms in calculating the conventional Shape Diameter Function. We have conducted extensive experiments with the Princeton Dataset. The results show that the proposed algorithm outperforms the state-of-the-art algorithms in this area. We have also applied the proposed algorithm to process the point cloud data acquired directly from the real 3D objects. It achieves excellent results too. These results demonstrate that the method proposed in this paper is effective and universal.

Efficient and robust segmentation and tracking of sperm cells in microscopic image sequences

Sperm motility analysis is an important factor in male fertility diagnosis. This article presents a hybrid segmentation method to detect sperm cells, which is robust to density variation of the cells in the image sequences. In addition, a preprocessing scheme is employed to remove fixed sperm cells and debris, which facilitate and speed up the cells' tracking stage. The article also proposes an automated sperm‐tracking algorithm in semen samples image sequences. It is a multi‐step tracking scheme, which is an enhanced version of adaptive window average speed (AWAS) tracking algorithm. It retrieves lost sperm cells during the tracking stage in adjacent frames and alleviates the cells collide problem. The proposed tracking algorithm provides both superior accuracy and higher speed compared to those of the other competitive algorithms for image sequences regardless of their particle densities.

A Region Merging Segmentation with Local Scale Parameters: Applications to Spectral and Elevation Data

Region merging is the most effective method for the segmentation of remote sensing data. The quality and the size of the resulted image objects is controlled by a global heterogeneity threshold, termed as the scale parameter. However, the multidimensional nature of the visible features in a scene defies the use of an even optimum single global scale parameter. In this study, a novel region merging segmentation method is proposed, where a local scale parameter is defined for each image object by its internal and external heterogeneity measures (i.e., local variance and Moran’s I). This method allows image objects with low internal and external heterogeneity to be further merged with higher scale parameter values, since they are more likely to be a part of an adjacent object, than objects with high internal and external heterogeneity. The proposed method was applied in spectral and elevation data and its results were evaluated visually and with supervised and unsupervised evaluation methods. The comparison with multi-resolution segmentation (MRS) showed that the proposed region merging method can produce improved segmentation results in terms of maximizing intra-object homogeneity and inter-object heterogeneity as well as in the delimitation of specific target objects, present in spectral and elevation data. The unsupervised evaluation results of the (1) Côte d’Azur, (2) Manchester, and (3) Szada images from the SZTAKI-INRIA building detection dataset showed that the proposed method (overall goodness, OGf (1): 0.7375, (2): 0.7923, (3): 0.7967) performs better than MRS (OGf (1): 0.7224, (2): 0.7648, (3): 0.7823). The higher values of OGf indicate their ability to produce segmentation results with reduced over-segmentation effects and without the need of presegmented input data, in contrast to the objective heterogeneity and relative homogeneity (OHRH) hybrid segmentation method (OGf (1): 0.5864, (2): 0.5151, (3): 0.6983).

Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images.

Cytological screening plays a vital role in the diagnosis of cancer from the microscope slides of pleural effusion specimens. However, this manual screening method is subjective and time-intensive and it suffers from inter- and intra-observer variations. In this study, we propose a novel Computer Aided Diagnosis (CAD) system for the detection of cancer cells in cytological pleural effusion (CPE) images. Firstly, intensity adjustment and median filtering methods were applied to improve image quality. Cell nuclei were extracted through a hybrid segmentation method based on the fusion of Simple Linear Iterative Clustering (SLIC) superpixels and K-Means clustering. A series of morphological operations were utilized to correct segmented nuclei boundaries and eliminate any false findings. A combination of shape analysis and contour concavity analysis was carried out to detect and split any overlapped nuclei into individual ones. After the cell nuclei were accurately delineated, we extracted 14 morphometric features, 6 colorimetric features, and 181 texture features from each nucleus. The texture features were derived from a combination of color components based first order statistics, gray level cooccurrence matrix and gray level run-length matrix. A novel hybrid feature selection method based on simulated annealing combined with an artificial neural network (SA-ANN) was developed to select the most discriminant and biologically interpretable features. An ensemble classifier of bagged decision trees was utilized as the classification model for differentiating cells into either benign or malignant using the selected features. The experiment was carried out on 125 CPE images containing more than 10500 cells. The proposed method achieved sensitivity of 87.97%, specificity of 99.40%, accuracy of 98.70%, and F-score of 87.79%.

Hybrid segmentation method based on multi‐scale Gaussian kernel fuzzy clustering with spatial bias correction and region‐scalable fitting for breast US images

Automated segmentation of tumors in breast ultrasound (US) images is challenging due to poor image quality, presence of speckle noise, shadowing effects and acoustic enhancement. This paper improves the multi‐scale Gaussian kernel induced fuzzy C‐means clustering method with spatial bias correction (MsGKFCM_S). Furthermore, it presents a hybrid segmentation method, using both the features of the MsGKFCM_S clustering and active contour driven by a region‐scalable fitting energy function. The result obtained from the MsGKFCM_S method is utilised to initialise the contour that spreads to identify the estimated regions. It also helps to estimate the several controlling parameters of the curve evolution process. The proposed approach is evaluated on a database of 127 breast US images consisting of 75 malignant and 52 solid benign cases. The performance of proposed approach is compared with other related techniques, using performance measures such as Jaccard Index, dice similarity, shape similarity, Hausdroff difference, area difference, accuracy and F‐measure. Results indicate that the proposed approach can successfully detect lesions in breast US images, with high accuracy of 97.889 and 97.513%. Moreover, the proposed approach has the capability of handling shadowing effects, acoustic enhancement and multiple lesions.