SHAPE CHARACTERIZATION OF MEDIASTINUM IN TUBERCULOSIS CHEST RADIOGRAPHS USING LEVEL SET SEGMENTATION

Mediastinum is considered as one of the substantial anatomical regions for the gross diagnosis of several chest related pathologies. The geometric variations of the mediastinum in Chest Radiographs (CXRs) could be utilised as potential image markers in the early detection of Tuberculosis (TB). This study attempts to segment mediastinum in CXRs using level sets for the shape characterization of TB conditions. The CXR images for this study are considered from a public database. An edge-based distance regularized level set evolution is employed to segment the lungs followed by a region-based Chan-Vese model that extracts mediastinum region. Features such as mediastinum area and lungs area are extracted from the segmented images. Further, mediastinum to lungs area ratio is calculated. Statistical analysis is performed on the features to differentiate normal and TB images. Results show that the proposed segmentation approach is able to segment the lungs and extract the mediastinum in CXRs. It is found that features namely mediastinum area and mediastinum to lungs area ratio are statistically significant in the differentiation of TB. Larger mediastinum area is observed in TB images as compared to normal. The performance of lung field segmentation is also observed to be in line with the literature. The mediastinum segmentation approach in CXRs obtains to be a novel method as compared to the existing methods. As the proposed approach based on mediastinum image analysis provides better shape characterization, the study could be clinically useful in the differentiation of TB conditions.

Modified distance regularized level set evolution for brain ventricles segmentation

Neurodegenerative disorders are commonly characterized by atrophy of the brain which is caused by neuronal loss. Ventricles are one of the prominent structures in the brain; their shape changes, due to their content, the cerebrospinal fluid. Analyzing the morphological changes of ventricles, aids in the diagnosis of atrophy, for which the region of interest needs to be separated from the background. This study presents a modified distance regularized level set evolution segmentation method, incorporating regional intensity information. The proposed method is implemented for segmenting ventricles from brain images for normal and atrophy subjects of magnetic resonance imaging and computed tomography images. Results of the proposed method were compared with ground truth images and produced sensitivity in the range of 65%–90%, specificity in the range of 98%–99%, and accuracy in the range of 95%–98%. Peak signal to noise ratio and structural similarity index were also used as performance measures for determining segmentation accuracy: 95% and 0.95, respectively. The parameters of level set formulation vary for different datasets. An optimization procedure was followed to fine tune parameters. The proposed method was found to be efficient and robust against noisy images. The proposed method is adaptive and multimodal.

A model for segmentation of CT images of liver lesions based on regional fitting and gradient information

Background: The Computerized tomography (CT) images of liver have such disadvantages as uneven gray scale, fuzzy boundary and missing, so the commonly used image segmentation model of liver lesions has low segmentation accuracy. Methods: We propose a new hybrid active contour model based on regional fitting and gradient information for segmenting CT images of liver lesions. Firstly, the problem of uneven gray scale of liver lesions image was solved by local area fitting method, and the gradient information of liver lesions image was integrated to enhance the detection ability of the model on the edge of liver lesions. Secondly, we introduce the region area term, which can keep the image segmentation curve smooth in the process of segmentation, and effectively control the direction and speed of curve evolution. Finally, the performance of the Distance Regularized Level Set Evolution (DRLSE) model, Region-Scalable Fitting (RSF) model and the present model was compared in the segmentation of liver lesions. Results: It can be concluded from the experimental results that: compared with DRLSE model and RSF model，the average Dice similarity coefficient reached 97.7%, ncreased by 12.7% and 11.7% respectively; the under segmentation rate was 2%, 9% and 17% lower, and the over segmentation rate was 1.6%. Conclusion: Therefore, the segmentation model proposed in this paper has excellent segmentation performance and greatly improves the segmentation accuracy of liver lesions.

Level Set Segmentation of Oil Spills from Earth Observatory Images Via Spatial KFCM Clustering

In this paper, we present a novel technique called spatial kernel fuzzy clustering with adaptive level set approach for Oil spill image segmentation. The proposed method is diversified into two stages; in the first stage the input is pre-processing by Spatial Kernel Fuzzy C-Means clustering (KFCM) to improve the clustering efficiency and less sensitive to noise. In the second stage, it necessary to use the level set method to refine the previous stage segmentation results. The performance of the level set segmentation is subjected to proper initialization and optimal formation of directing parameters. The controlling parameters of level set evolution are also projected after the results of kernel fuzzy clustering. The proposed method, spatial kernel fuzzy adaptive level set algorithm is enhanced the local minima problem. Such developments enable level set handling and more strong segmentation. The results confirm its effectiveness for oil spill images over the conventional CV model i.e number of iterations, Computational time and PSNR