Analysis of Color Images using Cluster based Segmentation Techniques

Fuzzy C-Means (FCM) clustering technique is among the most effective partitional clustering algorithms available in the literature. The Capacitated Vehicle Routing Problem (CVRP) is an important industrial logistics and managerial NP-hard problem. Cluster-First Route-Second Method (CFRS) is one of the efficient techniques used to solve CVRP. In CFRS technique, customers are first divided into clusters in the first phase, then each cluster is solved independently as a Traveling Salesman Problem (TSP) in the second phase. This research is concerned with the clustering phase of CFRS, and TSP is then solved using a traditional optimization method. Three supervised FCM based techniques are proposed to solve the clustering phase at reduced cost via centroids (pre-FCM) initialization phase. The proposed pre-FCM initialization techniques are developed to be problem dependent. Hence, three initialization techniques are first developed using K-means technique, spatially equally distributed, and demand weighted center of mass. Then, a modified demand weighted fuzzy c-means objective function is employed to assign customers to clusters. To compare the performance of the proposed supervised FCM techniques, forty-two CVRP benchmark problems are solved using the traditional fuzzy C-means algorithm and the developed algorithms. Extensive comparisons are conducted between the traditional fuzzy C-means algorithm, the three proposed initialization techniques, and other fuzzy C-means techniques available in the literature. Results show that the proposed three initialization techniques are efficient in terms of solution quality and computational cost.

The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k-NN algorithm, support vector machine, and hybrid data mining methods in accuracy.

The study of medical image analysis encompasses the various techniques for acquisition of images of biological structures pertaining to human body using radiations in different frequency ranges. The advancements in medical imaging over the past decades are enabling physicians to non-invasively peer inside the human body for the purpose of diagnosis and therapy. In this chapter, the objective is to focus on the studies relating to the analysis of endoscopic images of lower esophagus for abnormal region detection and identification of cancerous growth. Several color image segmentation techniques have been developed for automatic detection of cancerous regions in endoscopic images, which assists the physician for faster, proper diagnosis and treatment of the disease. These segmentation methods are evaluated for comparing their performances in different color spaces, namely, RGB, HSI, YCbCr, HSV, and CIE Lab. The segmented images are expected to assist the medical expert in drawing the biopsy samples precisely from the detected pathological legions. Further, various methods have been proposed for segmentation and classification of squamous cell carcinoma (SCC) from color microscopic images of esophagus tissue during pathological investigation. The efficacy of these methods has been demonstrated experimentally with endoscopic and microscopic image set and compared with manual segmentation done by medical experts. It is envisaged that the research in this direction eventually leads to the design and production of efficient intelligent computer vision systems for assisting the medical experts in their task of speedy accurate diagnosis of diseases and prescription of appropriate treatment of the patients.

This paper proposes a hybrid technique for color image segmentation. First an input image is converted to the image of CIE L*a*b* color space. The color features "a" and "b" of CIE L*a*b* are then fed into fuzzy C-means (FCM) clustering which is an unsupervised method. The labels obtained from the clustering method FCM are used as a target of the supervised feed forward neural network. The network is trained by the Levenberg-Marquardt back-propagation algorithm, and evaluates its performance using mean square error and regression analysis. The main issues of clustering methods are determining the number of clusters and cluster validity measures. This paper presents a method namely co-occurrence matrix based algorithm for finding the number of clusters and silhouette index values that are used for cluster validation. The proposed method is tested on various color images obtained from the Berkeley database. The segmentation results from the proposed method are validated and the classification accuracy is evaluated by the parameters sensitivity, specificity, and accuracy.

To acquire the fruit region of green apple image in close color more completely, this work designed a method of region extraction before fruit region combination. Firstly, channel images in every color space were analyzed to determine appropriate color channel images: images of G and b color channel in RGB and Lab color spaces, respectively. Then, these images were segmented by K-means clustering to obtain segmented images of G and b color channel. Post-processing was conducted to different segmentation results. For example, open operation and micro-region elimination were conducted to segmented image of G color channel. The same processing was conducted to b color channel image after combining with above processed G color channel image and filling holes. Therefore, the target fruit region can be acquired completely. The validity of this method was verified by experiment.

In the COVID-19 pandemic, computer vision plays a crucial role in crowd detection, supporting crowd restriction policies to mitigate virus spread. This research focuses on analyzing the impact of using the RGB LAB color space on the performance of NASNetMobile for crowd detection. The fine-tuning process, involving freezing layers in various NASNetMobile base model variations, is considered. Results reveal that the model with LAB color space outperforms model with RGB color space, with an average accuracy of 94.68% compared to 94.15%. From all the test iterations, it was found that the highest performance for the NASNetMobile model occurred when freezing 10% of the layers from the back for both model LAB and RGB color spaces, with the LAB color space achieving an accuracy of 95.4% and the RGB color space achieving an accuracy of 95.1%.

VANET-Based Intelligent Traffic Light Control System by Detecting Congestion Using Fuzzy C-Means Clustering Technique in a Smart City

In perceptually uniform color space, color differences can be measured in a way close to the human perception of two colors. A new feature descriptor, namely the color textons descriptor, is proposed in this paper according to the attribute of perceptually uniform color space. This descriptor can represent features via color differences in textons component images. It converts color image from RGB color space to CIE Lab color space, and then quantized the color images into 64 colors and find out the textons location in Lab color components. Finally, color differences are used to represent features and for image retrieval. Image retrieval experimental results have shown that the color textons descriptor has the discrimination power of color and texture features, and the performance is better than that of the edge histogram descriptor and LBP method significantly.

The topic of colour image segmentation has been and still is a hot issue in areas such as computer vision and image processing because of its wide range of practical applications. The urge has led to the development of numerous colour image segmentation algorithms to extract salient objects from colour images. However, because of the diverse imaging conditions in varying application domains, accuracy and robustness of several state-of-the-art colour image segmentation algorithms still leave room for further improvement. This dissertation reports on the development of a new image segmentation algorithm based on perceptual colour difference saliency along with binary morphological operations. The algorithm consists of four essential processing stages which are colour image transformation, luminance image enhancement, salient pixel computation and image artefact filtering. The input RGB colour image is first transformed into the CIE L*a*b colour image to achieve perceptual saliency and obtain the best possible calibration of the transformation model. The luminance channel of the transformed colour image is then enhanced using an adaptive gamma correction function to alleviate the adverse effects of illumination variation, low contrast and improve the image quality significantly. The salient objects in the input colour image are then determined by calculating saliency at each pixel in order to preserve spatial information. The computed saliency map is then filtered using the morphological operations to eliminate undesired factors that are likely present in the colour image. A series of experiments was performed to evaluate the effectiveness of the new perceptual colour difference saliency algorithm for colour image segmentation. This was accomplished by testing the algorithm on a large set of a hundred and ninety images acquired from four distinct publicly available benchmarks corporal. The accuracy of the developed colour image segmentation algorithm was quantified using four widely used statistical evaluation metrics in terms of precision, F-measure, error and Dice. Promising results were obtained despite the fact that the experimental images were selected from four different corporal and in varying imaging conditions. The results have indeed demonstrated that the performance of the newly developed colour image segmentation algorithm is consistent with an improved performance compared to a number of other saliency and non- saliency state-of-the-art image segmentation algorithms.

In this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of thea* andb* color channels rejectingL* and (2) a probabilistic approach ona* andb* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parametersa* andb* are not independent of the luminance parameterL* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017

Humans depend on their vision quality to check whether the fruit is ripe or unripe. They grade the maturity level of a fruit based on their vision based features that lead to inaccuracy, inconsistency and inefficiency in the results. There are numerous methods to detect the ripeness of fruits. The proposed work consists of two main steps; one is color image segmentation and other is comparison. For comparison purpose, we have used correlation and histogram matching techniques. The main technique used for image segmentation is region of interest based color segmentation, in which the red color region of image is separated. All the techniques use color images of fruits as input data. In the proposed work, all the images are first converted into LAB color space and then segmented both, the reference image and the input data image. With the help of correlation and histogram comparison of these images, the maturity level of a given fruit is determined.

Segmentation is the basic and important step for digital image analysis and understanding. Segmentation of acne lesions in the visual spectrum of light is very challenging due to factors such as varying skin tones due to ethnicity, camera calibration and the lighting conditions. In this approach the color image is transformed into various color spaces. The image is decomposed into the specified number of homogeneous regions based on the similarity of color using fuzzy C-means clustering technique. Features are extracted for each cluster and average values of these features are calculated. A new objective function is defined that selects the cluster holding the lesion pixels based on the average value of cluster features. In this study segmentation results are generated in four color spaces (RGB, rgb, YIQ, I1I2I3) and two individual color components (I3, Q). The number of clusters is varied from 2 to 6. The experiment was carried out on fifty images of acne patients. The performance of the proposed technique is measured in terms of the three mostly used metrics; sensitivity, specificity, and accuracy. Best results were obtained for Q and I3 color components of YIQ and I1I2I3 color spaces with the number of clusters equal to three. These color components show robustness against non-uniform illumination and maximize the gap between the lesion and skin color.

A two-layer dendritic lattice neural network is proposed to segment color images in the Red-Green-Blue (RGB) color space. The two layer neural network is a fully interconnected feed forward net consisting of an input layer that receives color pixel values, an intermediate layer that computes pixel interdistances, and an output layer used to classify colors by hetero-association. The two-layer net is first initialized with a finite small subset of the colors present in the input image. These colors are obtained by means of an automatic clustering procedure such as k-means or fuzzy c-means. In the second stage, the color image is scanned on a pixel by pixel basis where each picture element is treated as a vector and feeded into the network. For illustration purposes we use public domain color images to show the performance of our proposed image segmentation technique.

In this paper, we proposed a fuzzy c-means (FCM) cluster based adaptive thresholding segmentation algorithm for color image. The main advantage of this method is that, it does not require a priori knowledge about number of objects in the image. It calculates the threshold values automatically with the help of merging process. The first step of the method is that construct the histograms for each color channel. With this aim, information based histogram of the color intensities have been obtained. In the second step of the method, Fuzzy 2-partition is used on each of the three histograms in R(red), G(green) and B(blue) dimensions, color image segmentation is obtained the performance of the FCM cluster for each color channel. Experiment results show that this method can determine automatically the number of the thresholds levels and achieves good results for color images.

This paper proposes a color image segmentation algorithm based on affinity propagation clustering. Firstly, the color image is converted to other color space; and then color image is sampled, and the sampling data is clustered by affinity propagation algorithm with given the number, then we can get their class label of the rest data according to the maximum similarity rules; finally, using the regional combined methods ,we can remove independent small regions, and get revised image segmentation result. The simulation experiment shows that this method can achieve the application of affinity propagation algorithm in large-scale color image segmentation. And it has a faster processing speed and satisfactory segmentation results, more in line with the human whole visual perception.