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

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.

A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets.

Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image. As image pixels are generally unlabelled, the commonly used approach for the same is clustering. This paper reviews various existing clustering based image segmentation methods. Two main clustering methods have been surveyed, namely hierarchical and partitional based clustering methods. As partitional clustering is computationally better, further study is done in the perspective of methods belonging to this class. Further, literature bifurcates the partitional based clustering methods into three categories, namely K-means based methods, histogram-based methods, and meta-heuristic based methods. The survey of various performance parameters for the quantitative evaluation of segmentation results is also included. Further, the publicly available benchmark datasets for image-segmentation are briefed.

Automated microscopic image analysis for leukocytes identification: a survey.

Automatic quantification and classification of leukocytes in microscopic images are of paramount importance in the perspective of disease identification, its progress and drugs development. Extracting numerical values of leukocytes from microscopic images of blood or tissue sections represents a tricky challenge. Research efforts in quantification of these cells include normalization of images, segmentation of its nuclei and cytoplasm followed by their classification. However, there are several related problems viz., coarse background, overlapped nuclei, conversion of 3-D nuclei into 2-D nuclei etc. In this review, we have categorized, evaluated, and discussed recently developed methods for leukocyte identification. After reviewing these methods and finding their constraints, a future research perspective has been presented. Further, the challenges faced by the pathologists with respect to these problems are also discussed.

Feature selection and classification of leukocytes using random forest.

In automatic segmentation of leukocytes from the complex morphological background of tissue section images, a vast number of artifacts/noise are also extracted causing large amount of multivariate data generation. This multivariate data degrades the performance of a classifier to discriminate between leukocytes and artifacts/noise. However, the selection of prominent features plays an important role in reducing the computational complexity and increasing the performance of the classifier as compared to a high-dimensional features space. Therefore, this paper introduces a novel Gini importance-based binary random forest feature selection method. Moreover, the random forest classifier is used to classify the extracted objects into artifacts, mononuclear cells, and polymorphonuclear cells. The experimental results establish that the proposed method effectively eliminates the irrelevant features, maintaining the high classification accuracy as compared to other feature reduction methods.