Partitioning intensity inhomogeneity colour images via Saliency-based active contour

Partitioning or segmenting intensity inhomogeneity colour images is a challenging problem in computer vision and image shape analysis. Given an input image, the active contour model (ACM) which is formulated in variational framework is regularly used to partition objects in the image. A selective type of variational ACM approach is better than a global approach for segmenting specific target objects, which is useful for applications such as tumor segmentation or tissue classification in medical imaging. However, the existing selective ACMs yield unsatisfactory outcomes when performing the segmentation for colour (vector-valued) with intensity variations. Therefore, our new approach incorporates both local image fitting and saliency maps into a new variational selective ACM to tackle the problem. The euler-lagrange (EL) equations were presented to solve the proposed model. Thirty combinations of synthetic and medical images were tested. The visual observation and quantitative results show that the proposed model outshines the other existing models by average, with the accuracy of 2.23% more than the compared model and the Dice and Jaccard coefficients which were around 12.78% and 19.53% higher, respectively, than the compared model

Segmentation and characterization of masses in breast ultrasound images using active contour

The active contour or Snake is a computer generated curve, used to trace boundaries of images. This paper presents the application of Snake for the segmentation of masses on breast ultrasound images and the characterization of the segmented masses as malignant or benign. Initially, the Balloon Snake is chosen to segment the masses. Comparison on the masses areas segmented by the Balloon Snake is done against the areas traced by radiologist. Experimental result shows that from fifty masses tested, the Balloon Snake successfully segment the masses with accuracy of 95.71%. Then, a mass is characterized as benign or malignant using a proposed method namely the semi-automated characterization (SAC) method. The method is based on the segmented masses produced by the Balloon Snake. The criterion of angular margin is considered in characterizing the masses as malignant or benign by the SAC method. The characterization reading of a mass by the SAC method is compared with thirty sets of characterization readings of a mass by different radiologists. The comparison is made in terms of sensitivity and specificity values. Based on the values, the receiver operating characteristics (ROC) curve is plotted for each set of comparison. From the thirty sets of comparisons, it is found that the area under curve of all the thirty ROC curves are greater than 0.7. The value implies that the SAC method gives high accuracy in characterizing benign from malignant mass. Since the method is based on the segmented masses by the Balloon Snake, the value also implies that the accuracy of Balloon Snake in segmenting the images is high (95.71%)

Microcalcifications segmentation using three edge detection techniques

Edge detection has been widely used especially in medical image processing field. In this paper we are comparing Sobel, Prewitt and Laplacian of Gaussian (LoG) edge detection techniques in segmenting the boundary of microcalcifications. The edge detection must satisfy the breast phantom scoring criteria before the segmentation phase is carried out. Then, all of the edge detection techniques are implemented in the Enhanced Distance Active Contour (EDAC) model for the segmentation process. Results obtained from Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve shows that the Prewitt edge detection has the highest value of AUC, followed by the Sobel and LoG which are 0.79, 0.72 and 0.71 respectively

Comparison between GVF snake and ED snake in segmenting microcalcifications

Snake, active contour or deformable active contour has been widely used in medical image segmentation area. In this paper, comparison between Gradient Vector Flow (GVF) snake and Enhanced Distance (ED) snake in segmenting microcalcifications is carried out. The performance is measured based on actual area of the average percentage difference traced by expert radiologists. Results obtained shows that the values of average percentage difference for the GVF and ED snake are 4.3% and 6.68% respectively. These results indicate that the GVF snake has better performance with 95.7%

Selective Image Segmentation Models Using Three Distance Functions

Image segmentation can be defined as partitioning an image that contains multiple segments of meaningful parts for further processing. Global segmentation is concerned with segmenting the whole object of an observed image. Meanwhile, the selective segmentation model is concerned with segmenting a specific object required to be extracted. The Convex Distance Selective Segmentation (CDSS) model, which uses the Euclidean distance function as the fitting term, was proposed in 2015. However, the Euclidean distance function takes time to compute. This paper proposes the reformulation of the CDSS minimization problem by changing the fitting term with three popular distance functions, namely Chessboard, City Block, and Quasi-Euclidean. The proposed models are CDSSNEW1, CDSSNEW2, and CDSSNEW3, which apply the Chessboard, City Block, and Quasi-Euclidean distance functions respectively. In this study, the Euler-Lagrange (EL) equations of the proposed models were derived and solved using the Additive Operator Splitting method. Then, MATLAB coding was developed to implement the proposed models. The accuracy of the segmented image was evaluated using the Jaccard (JSC) and Dice Similarity Coefficients (DSC). The execution time was recorded to measure the efficiency of the models. Numerical results showed that the proposed CDSSNEW1 model based on the Chessboard distance function could segment the specific object successfully for all grayscale images with the fastest execution time compared to other models

Selective Segmentation Model for Vector-Valued Images

One of the most important steps in image processing and computer vision for image analysis is segmentation, which can be classified into global and selective segmentations. Global segmentation models can segment whole objects in an image. Unfortunately, these models are unable to segment a specific object that is required for extraction. To overcome this limitation, the selective segmentation model, which is capable of extracting a particular object or region in an image, must be prioritised. Recent selective segmentation models have shown to be effective in segmenting greyscale images. Nevertheless, if the input is vector-valued or identified as a colour image, the models simply ignore the colour information by converting that image into a greyscale format. Colour plays an important role in the interpretation of object boundaries within an image as it helps to provide a more detailed explanation of the scene’s objects. Therefore, in this research, a model for selective segmentation of vector-valued images is proposed by combining concepts from existing models. The finite difference method was used to solve the resulting Euler-Lagrange (EL) partial differential equation of the proposed model. The accuracy of the proposed model’s segmentation output was then assessed using visual observation as well as by using two similarity indices, namely the Jaccard (JSC) and Dice (DSC) similarity coefficients. Experimental results demonstrated that the proposed model is capable of successfully segmenting a specific object in vector-valued images. Future research on this area can be further extended in three-dimensional modelling

RESTORATION OF OLD MALAY JAWI MANUSCRIPTS USING MUMFORD-SHAH AND BERTALMIO INPAINTING MODELS

Jawi is the earliest writing script of the Malay Archipelago that was derived from Arabic letters. Old Malay Jawi manuscripts may provide vital information about the legacy, cultures, and historical evolution of the Malay Archipelago over time. However, while preserved, old Malay Jawi manuscripts tend to be damaged. Image inpainting is a process of reconstructing missing parts of an image which can be used to restore the old Malay Jawi manuscripts. The Mumford-Shah and Bertalmio inpainting models are two well-known and effective methods for solving the image inpainting problem. Hence, the aim of this study is to determine which model is better at restoring corrupted input images of the old Malay Jawi manuscripts. A sample of thirty (30) old Malay Jawi manuscript images were obtained from Kumpulan Penyelidikan Etnomatematik Melayu (KUPELEMA). The corrupted images were restored using both models, implemented using the MATLAB software. The Structural Similarity Index Measure (SSIM) and Mean Absolute Error (MAE) were utilized to assess the quality of the results. The numerical experiment demonstrates that the average values of SSIM and MAE for Mumford-Shah inpainting model are 0.9380 and 0.0151 respectively, while the values for the Bertalmio inpainting model are 0.8762 and 0.0255 respectively. This indicates that the Mumford-Shah inpainting model is more effective than Bertalmio inpainting model. The algorithms used in this study can be upgraded to a software framework for commercial use and can be implemented for other kinds of digitized data

Boundary extraction of abnormality region in breast mammography image using active contours

Mammography is a screening tool for breast cancer detection that produces grayscale images of the breast. The fundamental problem in mammography image analysis is to extract the boundary of breast abnormality from its healthy background tissues. The process is also known as the image segmentation. The procedure is necessary for further clinical diagnosis and monitoring in Computer Aided Detection (CAD) analysis systems. Active contour method has been proven to be effective to extract boundary of an image. The recent and effective selective type of active contour model, termed Primal Dual Selective Segmentation (PDSS) model, was proposed in 2019. However, the PDSS model having problem in segmenting images with low contrast. It is known that low contrast image is commonly encountered in mammography images that can result to poor boundary extraction. Thus, the aim of this study is to modify the PDSS model to extract the boundary of abnormality region in mammography images. The modification is made by considering three different image enhancement algorithms which are histogram equalization, histogram stretching and adaptive histogram equalization as the new fitting terms in the PDSS model and these results in three variants of modified PDSS models termed as PDSS1, PDSS2 and PDSS3 respectively. The efficiency of the proposed models was then assessed by recording the computation time while the accuracy of the extracted image boundary was evaluated using the Jaccard (JSC) and Dice Similarity Coefficients (DSC). Numerical experiments demonstrated that the proposed PDSS2 model based on histogram stretching achieved the highest segmentation accuracy with the fastest computational speed compared to other models. In future, the proposed model can be extended into the threedimensional and colour formulations

Segmentation of masses from breast ultrasound images using parametric active contour algorithm

The active contour (Snake) is a computer generated curve that can trace boundaries of images. As a method which applies the computer technology in mathematics, Snake is computationally formulated based on controlled continuous splines and adopts the mathematical concept of energy minimization. This paper presents the application of Snake for the segmentation of masses on breast ultrasound images. The images used are taken from Malaysian population. The boundaries of the masses identified may be used in classification of cancers or non-cancerous masses. Specifically the Balloon Snake is applied in segmenting the masses in the breast ultrasound images. Comparison on the masses areas segmented by the Balloon Snake is done against the areas traced by an expert (radiologist). It is found that from forty-five masses tested, the average percentage area difference of Balloon Snake is 4.47%. This implies that the accuracy of segmentation results for the Balloon Snake is 95.53%