A New Texture Based Segmentation Method to Extract Object from Background

Extraction of object regions from complex background is a hard task and it is an essential part of image segmentation and recognition. Image segmentation denotes a process of dividing an image into different regions. Several segmentation approaches for images have been developed. Image segmentation plays a vital role in image analysis. According to several authors, segmentation terminates when the observer2019;s goal is satisfied. The very first problem of segmentation is that a unique general method still does not exist: depending on the application, algorithm performances vary. This paper studies the insect segmentation in complex background. The segmentation methodology on insect images consists of five steps. Firstly, the original image of RGB space is converted into Lab color space. In the second step 2018;a2019; component of Lab color space is extracted. Then segmentation by two-dimension OTSU of automatic threshold in 2018;a-channel2019; is performed. Based on the color segmentation result, and the texture differences between the background image and the required object, the object is extracted by the gray level co-occurrence matrix for texture segmentation. The algorithm was tested on dreamstime image database and the results prove to be satisfactory

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications

Implementation of Thermal and Spectral Image Analysis for Neuropathic Foot

Diabetes is a grave metabolic disease described by high glucose levels. The feet of pa-tients with diabetes are at the danger of a variety of neurotic results including peripheral vascular infection, disfigurement, ulceration, and necrosis (infection caused by localized death of living cells or tissue) leading to amputation. The way to deal with the diabetic foot is anticipation and early location. Sadly, currently health provider’s focus on re-sponsive diabetes mind and the accessibility of lacking subjective demonstrative screen-ing methodology makes doctors miss the finding of a few patients. The main objective is that diabetic foot demonstrates basic neuropathic and vascular symptoms. When a foot patient is inactive, the thermal recuperation will be much slow-er. This thermal response speed can be used as a quantitative measure for the study of diabetic foot condition. In our study, thermal recovery of the foot following cold pressure is discovered using a thermal camera. The captured thermal image is then analysed, and the temperature re-covery at each point on the foot is extracted and calibrated using a thermal control ap-pears, and the precarious regions are recognized. In addition, LED-based spectral imag-ing is tested to estimate oxygen saturation in the foot. In this subject, we show our examinations on the following parts of the implementation of medical application analytic system based on: measurement protocols, thermal image segmentation, new techniques to perform model analysis of gathered images, and our preliminary discoveries focused on small scale clinical investigation of some patients, which demonstrate the potential of the diagnostic system.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Digital Image Analysis of Vitiligo for Monitoring of Vitiligo Treatment

Vitiligo is an acquired pigmentary skin disorder characterized by depigmented macules that result from damage to and destruction of epidermal melanocytes. Visually, the vitiligous areas are paler in contrast to normal skin or completely white due to the lack of pigment melanin. The course of vitiligo is unpredictable where the vitiligous skin lesions may remain stable for years before worsening. Vitiligo treatments have two objectives, to arrest disease progression and to re-pigment the vitiligous skin lesions. To monitor the efficacy of the treatment, dermatologists observe the disease directly, or indirectly using digital photos. Currently there is no objective method to determine the efficacy of the vitiligo treatment. Physician's Global Assessment (PGA) scale is the current scoring system used by dermatologists to evaluate the treatment. The scale is based on the degree of repigmentation within lesions over time. This quantitative tool however may not be help to detect slight changes due to treatment as it would still be largely dependent on the human eye and judgment to produce the scorings. In addition, PGA score is also subjective, as it varies with dermatologists. The progression of vitiligo treatment can be very slow and can take more than 6 months. It is observed that dermatologists find it visually hard to determine the areas of skin repigmentation due to this slow progress and as a result the observations are made after a longer time frame. The objective of this research is to develop a tool that enables dermatologists to determine and quantify areas of repigmentation objectively over a shorter time frame during treatment. The approaches towards achieving this objective are based on digital image processing techniques. Skin color is due to the combination of skin histological parameters, namely pigment melanin and haemoglobin. However in digital imaging, color is produced by combining three different spectral bands, namely red, green, and blue (RGB). It is believed that the spatial distribution of melanin and haemoglobin in skin image could be separated. It is found that skin color distribution lies on a two-dimensional melanin-haemoglobin color subspace. In order to determine repigmentation (due to pigment melanin) it is necessary to perform a conversion from RGB skin image to this two-dimensional color subspace. Using principal component analysis (PCA) as a dimensional reduction tool, the two-dimensional subspace can be represented by its first and second principal components. Independent component analysis is employed to convert the twodimensional subspace into a skin image that represents skin areas due to melanin and haemoglobin only. In the skin image that represents skin areas due to melanin, vitiligous skin lesions are identified as skin areas that lack melanin. Segmentation is performed to separate the healthy skin and the vitiligous lesions. The difference in the vitiligous surface areas between skin images before and after treatment will be expressed as a percentage of repigmentation in each vitiligo lesion. This percentage will represent the repigmentation progression of a particular body region. Results of preliminary and pre-clinical trial study show that our vitiligo monitoring system has been able to determine repigmentation progression objectively and thus treatment efficacy on a shorter time cycle. An intensive clinical trial is currently undertaken in Hospital Kuala Lumpur using our developed system. VI