Novel Algorithm For Segmentation Research Articles

Automatic tracing and extraction of text‐line and word segments directly in JPEG compressed document images

JPEG is one of the popular and efficient compression algorithms supported in the consumer electronics world. Excessive usage of mobile phones and e-governance applications have all resulted in a huge collection of JPEG compressed document images. The major challenge with these images is that its processing becomes expensive as it requires repeated decompression and recompression operations. Recently, it has been proved that developing algorithms to operate directly on the compressed data is one of the solutions in overcoming the above issue. This research study investigates a novel algorithm for segmentation of text-lines and words directly from JPEG compressed handwritten document images. Segmenting a handwritten document is challenging due to the presence of uneven spacing, variable font sizes, overlapping and touching components, and it becomes much more challenging if it is to be done directly in the compressed image. The proposed technique virtually fixes a vertical stripe at the beginning of the document to detect starting points of text-lines. Then a moving window-based space penetration algorithm is used for tracing the exact line boundary between two text-lines, resolving the issues of space and font variations, touching and overlapping components. Subsequently, a word boundary tracing algorithm is used to segment words.

A thumbnail-based hierarchical fuzzy clustering algorithm for SAR image segmentation

This paper proposes a novel algorithm for segmentation of synthetic aperture radar (SAR) image, our proposed algorithm (THFCM) is based on thumbnail representations and a hierarchical fuzzy C-means (FCM) approach. THFCM firstly divides the image into pixel groups to extract local feature, and the major pixels of each pixel group are selected to construct a thumbnail. FCM is then used to segment each thumbnail, and hierarchical segmentation is then performed on the overall image data, based on the results of thumbnail clustering. The thumbnail approach leverages local image information, helping to overcome speckle noise, while the hierarchical approach improves computational efficiency. Experiments on simulated and real SAR images suggest that THFCM outperforms several other state-of-the-art algorithms in terms of both segmentation accuracy and running time.

A Novel Algorithm for Segmentation of Solitary Pulmonary Nodules in Chest Computed Tomography Based on Three-Dimensional Connected Voxels

Background: With the increasing incidence of lung cancer, it is prudent to do screenings for individuals at high-risk. Solitary pulmonary nodules (SPNs) are an indication of small tumors or early stages of disease. Therefore, accurate detection of SPNs is important to both clinicians and radiologists. Since a large number of computed tomography (CT) scans are being acquired during a lung cancer screening, there is an urgent need for new automated techniques to detect SPNs. Methods: A novel algorithm for segmentation of SPNs in CT scans based on three-dimensional connected voxels (3DCVs) can be used to screen out potential patients with SPNs. 120 cases of CT scans from a public database (100 positive cases with nodules and 20 negative cases without nodules) and 30 negative cases from the routine CT scans completed in a hospital were used to test the algorithm. The algorithm is based on the fact that most pulmonary nodules are solitary at their early stages. First, find suitable CT values thresholds for CT values to convert pulmonary nodules, normal tissues and air spaces in each chest CT slice into black and white images. Then stack the slices in their originally physical order. This will produce a three-dimensional (3D) matrix with pulmonary nodules and normal tissues constructing their own 3DCVs respectively. Results: Of the 100 positive cases, 93 cases showed positive detection of SPNs and 7 cases did not. Of the 50 negative cases, 48 cases returned a negative result and 2 cases showed as positive result. In this study, the sensitivity is 93% and the specificity is 96% with a 4% false positive rate (FPR). Conclusions: This algorithm can be used to screen out positive chest CT scans efficiently, which will increase efficiency by two to three times than when compared with manual inspection and detection.

A Novel Algorithm for Segmentation of Parasites in Thin Blood Smears From Microscopy Using Type II Fuzzy Sets and Inverse Gaussian Gradient

Malaria is the leading health problem globally in the equatorial regions. Even after campaigning and control by the World Health Organization (WHO), malaria is a high mortality-causing infectious disease due to inappropriate and late diagnosis. To prevent getting affected by malaria, the diagnosis should be in the early stage and accurate. This research presents an innovative approach for erythrocyte segmentation and malaria parasite detection from microscopic images. In addition, the author utilizes Einstein t-conorm function to generate new fuzzy membership function which is used to segment the infected regions of the blood cells in microscopic images. Finally, the inverse Gaussian gradient function is used for the final segmentation. This technique is compared with state-of-the-art segmentation techniques such as k-means, fuzzy c-means segmentation, and spatial intuitionistic fuzzy c-means (SIFCM) segmentation approaches. Experimental results are validated qualitatively, and it is deduced that the proposed method is the robust segmentation method for malaria parasite detection.

A novel algorithm for segmentation of leukocytes in peripheral blood

Abstract In the detection of anemia, leukemia and other blood diseases, the number and type of leukocytes are essential evaluation parameters. However, the conventional leukocyte counting method is not only quite time-consuming but also error-prone. Consequently, many automation methods are introduced for the diagnosis of medical images. It remains difficult to accurately extract related features and count the number of cells under the variable conditions such as background, staining method, staining degree, light conditions and so on. Therefore, in order to adapt to various complex situations, we consider RGB color space, HSI color space, and the linear combination of G, H and S components, and propose a fast and accurate algorithm for the segmentation of peripheral blood leukocytes in this paper. First, the nucleus of leukocyte was separated by using the stepwise averaging method. Then based on the interval-valued fuzzy sets, the cytoplasm of leukocyte was segmented by minimizing the fuzzy divergence. Next, post-processing was carried out by using the concave-convex iterative repair algorithm and the decision mechanism of candidate mask sets. Experimental results show that the proposed method outperforms the existing non-fuzzy sets methods Among the methods based on fuzzy sets, the interval-valued fuzzy sets perform slightly better than interval-valued intuitionistic fuzzy sets and intuitionistic fuzzy sets.

Segmentation of video background regions based on a DTCNN-clustering approach

We propose a novel algorithm for segmentation of video background models in time-variant scenarios. It is robust to gradual or abrupt illumination changes, diverse kind of noises, and even scenario variation. The algorithm generates regions according to the scene composition by keeping region segmentation coherence. The proposed method based on a discrete-time cellular neural network estimates the number regions in the current background model, and then, a modified k-means algorithm is used to achieve segmentation. The findings demonstrate the robustness of the method and its superiority over two state of the art scene segmentation algorithms.

Semi-automated analysis of infarct heterogeneity on DE-MRI using graph cuts

Background Two popular methods for determining the threshold values for the infarct core and gray zone on delayed enhancement MR images (DE-MRI) have been proposed previously: full width and half maximum (FWHM) and standard deviation (SD) methods [1]. Major limitations of these methods are:1) three manually drawn contours are needed for endocardial, epicardial and remote myocardium boundaries, which is time consuming and suffers from inter-observer and intra-observer variability; 2) the difficulty in reproducible manual delineation of remote myocardium, is an important contributor to variability in results; and 3) the dependence on the remote region statistics is problematic due to the low SNR of this region [2]. The purpose of this research was to develop a novel algorithm for segmentation of infarct core and gray zone from conventional IR-GRE shortaxis MR images with highly robust and reproducible results comparable to the FWHM analysis while eliminating the requirement for drawing a remote myocardial region.

Segmentation of Natural Images by Texture and Boundary Compression

We present a novel algorithm for segmentation of natural images that harnesses the principle of minimum description length (MDL). Our method is based on observations that a homogeneously textured region of a natural image can be well modeled by a Gaussian distribution and the region boundary can be effectively coded by an adaptive chain code. The optimal segmentation of an image is the one that gives the shortest coding length for encoding all textures and boundaries in the image, and is obtained via an agglomerative clustering process applied to a hierarchy of decreasing window sizes as multi-scale texture features. The optimal segmentation also provides an accurate estimate of the overall coding length and hence the true entropy of the image. We test our algorithm on the publicly available Berkeley Segmentation Dataset. It achieves state-of-the-art segmentation results compared to other existing methods.

Segmentation with gray-scale connectedness can separate arteries and veins in MRA.

To describe and present some preliminary results for a novel algorithm for segmentation with gray-scale connectedness as a means to separate arteries and veins in magnetic resonance angiography (MRA). The proposed algorithm, SeparaSeed, uses the gray-scale degree of connectedness as a tool to find the zone surrounding each vessel, in order to split the original volume into its different vessel components. In contrast to traditional segmentation methods, no gray-scale information is lost in the process. The segmentation is performed in one step, resulting in a partition of the initial volume into a chosen number of regions of interest (ROIs). Finally, visualization is achieved by projecting the 3D vessel trees to 2D using the common maximum intensity projection (MIP). The algorithm was tested in two MRA data sets of the vessels of the pelvis acquired after injection of an intravascular contrast agent and in one data set of the vessels of the neck with gadolinium. In all data sets, a large proportion of the venous signal was removed while preserving that of the arteries, thus improving visualization of the relevant vessels. Separation of arteries and veins is feasible with the proposed algorithm with a moderate amount of interaction.

Fast and automatic video object segmentation and tracking for content-based applications

The new video-coding standard MPEG-4 enables content-based functionality, as well as high coding efficiency, by taking into account shape information of moving objects. A novel algorithm for segmentation of moving objects in video sequences and extraction of video object planes (VOPs) is proposed . For the case of multiple video objects in a scene, the extraction of a specific single video object (VO) based on connected components analysis and smoothness of VO displacement in successive frames is also discussed. Our algorithm begins with a robust double-edge map derived from the difference between two successive frames. After removing edge points which belong to the previous frame, the remaining edge map, moving edge (ME), is used to extract the VOP. The proposed algorithm is evaluated on an indoor sequence captured by a low-end camera as well as MPEG-4 test sequences and produces promising results.