Dataset Information

A new firefly algorithm-based superpixel clustering method for vehicle segmentation.

ABSTRACT: The vehicle segmentation in the images of a crowded and unstructured road traffic, having inconsistent driving patterns and vivid attributes like colour, shapes, and size, is a complex task. For the same, this paper presents a new firefly algorithm-based superpixel clustering method for vehicle segmentation. The proposed method introduces a modified firefly algorithm by incorporating the best solution for enhancing the exploitation behaviour and solution precision. The modified firefly algorithm is further used to obtain the optimal superpixel clusters. The modified firefly algorithm is compared against state-of-the-art meta-heuristic algorithms on IEEE CEC 2015 benchmark problems in terms of mean fitness value, Wilcoxon rank-sum test, convergence behaviour, and box plot. The proposed meta-heuristic algorithm performed superior on more than 80% of the considered benchmark problems. Moreover, the modified firefly algorithm is statistically better on more than 92% of the total problems during Wilcoxon test. Further, the proposed segmentation method is analysed on a traffic dataset to segment the auto-rickshaw. The performance of the proposed method has been compared with kmeans-based superpixel clustering method. The proposed method shows the highest mean value of 0.6242 for Dice coefficient. Both qualitative and quantitative results affirm the efficacy of the proposed method.

SUBMITTER: Tiwari T

PROVIDER: S-EPMC9190197 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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A new firefly algorithm-based superpixel clustering method for vehicle segmentation.

Tiwari Twinkle T Saraswat Mukesh M

Soft computing 20220613

The vehicle segmentation in the images of a crowded and unstructured road traffic, having inconsistent driving patterns and vivid attributes like colour, shapes, and size, is a complex task. For the same, this paper presents a new firefly algorithm-based superpixel clustering method for vehicle segmentation. The proposed method introduces a modified firefly algorithm by incorporating the best solution for enhancing the exploitation behaviour and solution precision. The modified firefly algorithm ...[more]

PMID: 35729951

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Project description:Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) pipeline for automated liver segmentation. The entire CT images were first partitioned into superpixel regions, where nearby pixels with similar CT number were aggregated. Secondly, we converted the conventional binary segmentation into a multinomial classification by labeling the superpixels into three classes: interior liver, liver boundary, and non-liver background. By doing this, the boundary region of the liver was explicitly identified and highlighted for the subsequent classification. Thirdly, we computed an entropy-based saliency map for each CT volume, and leveraged this map to guide the sampling of image patches over the superpixels. In this way, more patches were extracted from informative regions (e.g. the liver boundary with irregular changes) and fewer patches were extracted from homogeneous regions. Finally, deep CNN pipeline was built and trained to predict the probability map of the liver boundary. We tested the proposed algorithm in a cohort of 100 patients. With 10-fold cross validation, the SBBS-CNN achieved mean Dice similarity coefficients of 97.31??±??0.36% and average symmetric surface distance of 1.77??±??0.49?mm. Moreover, it showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms. SBBS-CNN provides an accurate and effective tool for automated liver segmentation. It is also envisioned that the proposed framework is directly applicable in other medical image segmentation scenarios.

Dataset Information

A new firefly algorithm-based superpixel clustering method for vehicle segmentation.

Publications

A new firefly algorithm-based superpixel clustering method for vehicle segmentation.

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