Approximate Lesion Localization in Dermoscopy Images

Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. Methods: In this article, we present an approximate lesion localization method that serves as a preprocessing step for detecting borders in dermoscopy images. In this method, first the black frame around the image is removed using an iterative algorithm. The approximate location of the lesion is then determined using an ensemble of thresholding algorithms. Results: The method is tested on a set of 428 dermoscopy images. The localization error is quantified by a metric that uses dermatologist determined borders as the ground truth. Conclusion: The results demonstrate that the method presented here achieves both fast and accurate localization of lesions in dermoscopy images

Prediction model of alcohol intoxication from facial temperature dynamics based on K-means clustering driven by evolutionary computing

Alcohol intoxication is a significant phenomenon, affecting many social areas, including work procedures or car driving. Alcohol causes certain side effects including changing the facial thermal distribution, which may enable the contactless identification and classification of alcohol-intoxicated people. We adopted a multiregional segmentation procedure to identify and classify symmetrical facial features, which reliably reflects the facial-temperature variations while subjects are drinking alcohol. Such a model can objectively track alcohol intoxication in the form of a facial temperature map. In our paper, we propose the segmentation model based on the clustering algorithm, which is driven by the modified version of the Artificial Bee Colony (ABC) evolutionary optimization with the goal of facial temperature features extraction from the IR (infrared radiation) images. This model allows for a definition of symmetric clusters, identifying facial temperature structures corresponding with intoxication. The ABC algorithm serves as an optimization process for an optimal cluster's distribution to the clustering method the best approximate individual areas linked with gradual alcohol intoxication. In our analysis, we analyzed a set of twenty volunteers, who had IR images taken to reflect the process of alcohol intoxication. The proposed method was represented by multiregional segmentation, allowing for classification of the individual spatial temperature areas into segmentation classes. The proposed method, besides single IR image modelling, allows for dynamical tracking of the alcohol-temperature features within a process of intoxication, from the sober state up to the maximum observed intoxication level.Web of Science118art. no. 99

Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm

Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively

Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segmentation

This paper proposes a new method for image segmentation is hybrid multilevel thresholding and improved harmony search algorithm. Improved harmony search algorithm which is a method for finding vector solutions by increasing its accuracy. The proposed method looks for a random candidate solution, then its quality is evaluated through the Otsu objective function. Furthermore, the operator continues to evolve the solution candidate circuit until the optimal solution is found. The dataset used in this study is the retina dataset, tongue, lenna, baboon, and cameraman. The experimental results show that this method produces the high performance as seen from peak signal-to-noise ratio analysis (PNSR). The PNSR result for retinal image averaged 40.342 dB while for the average tongue image 35.340 dB. For lenna, baboon and cameramen produce an average of 33.781 dB, 33.499 dB, and 34.869 dB. Furthermore, the process of object recognition and identification is expected to use this method to produce a high degree of accuracy

Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia.

The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique

EDGE DETECTION PARAMETER OPTIMIZATION BASED ON THE GENETIC ALGORITHM FOR RAIL TRACK DETECTION

One of the most important parameters in an edge detection process is setting up the proper threshold value. However, that parameter can be different for almost each image, especially for infrared (IR) images. Traditional edge detectors cannot set it adaptively, so they are not very robust. This paper presents optimization of the edge detection parameter, i.e. threshold values for the Canny edge detector, based on the genetic algorithm for rail track detection with respect to minimal value of detection error. First, determination of the optimal high threshold value is performed, and the low threshold value is calculated based on the well-known method. However, detection results were not satisfactory so that, further on, the determination of optimal low and high threshold values is done. Efficiency of the developed method is tested on set of IR images, captured under night-time conditions. The results showed that quality detection is better and the detection error is smaller in the case of determination of both threshold values of the Canny edge detector

A novel modified SFTA approach for feature extraction.

To increase the efficiency of conventional Segmentation Based Fractal Texture Analysis (SFTA), we propose a new approach on SFTA algorithm. We use an optimum multilevel thresholding hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called HGAPSO with the optimization technique for classification based on grey level range to get more accurate output. Experimental results show that proposed approach exhibits average 2% higher classification accuracy than conventional SFTA for our tested dataset

An improved adaptive genetic algorithm for image segmentation and vision alignment used in microelectronic bonding

In order to improve the precision and efficiency of microelectronic bonding, this paper presents an improved adaptive genetic algorithm (IAGA) for the image segmentation and vision alignment of the solder joints in the microelectronic chips. The maximum between-cluster variance (OTSU) threshold segmentation method was adopted for the image segmentation of microchips, and the IAGA was introduced to the threshold segmentation considering the features of the images. The performance of the image segmentation was investigated by computational and experimental tests. The results show that the IAGA has faster convergence and better global optimality compared with standard genetic algorithm (SGA), and the quality of the segmented images becomes better by using the OTSU threshold segmentation method based on IAGA. On the basis of moment invariant approach, the microvision alignment was realized. Experiments were carried out to implement the microvision alignment of the solder joints in the microelectronic chips, and the results indicate that there are no alignment failures using the OTSU threshold segmentation method based on IAGA, which is superior to the OTSU method based on SGA in improving the precision and speed of the vision alignments