Image Analysis for Segmentation of Psoriasis Lesion

Psoriasis, a hereditary inflammatory skin condition that currently affects 2 - 3 % of the world's population, is marked by reddish, scaly rashes or lesions covered with scabs of dead skin. lbis dermatosis is currently not curable but the symptoms can be effectively controlled through an accurate assessment scheme and well-integrated medical care therapy. The National Psoriasis Foundation Medical Board has published a guideline that categorizes the severity of psoriasis - mild, moderate and severe, each characterized by the percentage of lesions on an individual's body surface area. However the caveat remains that the distinction between the different categories of severity is largely influenced by the clinical practitioner's subjectivity. As a result, PASI scoring is introduced. PASI (Psoriasis Area and Severity Index) is currently the gold standard method to measure psoriasis severity by evaluating the area, erythema, scaliness and thickness of the lesions. These 4 parameters require the lesions to be first segmented from the skin patches before they can be assessed and scored individually. lbis report thus investigates digital image analysis techniques to segment psoriasis lesions. In this work, 90 patients are categorized into groups of differing skin tones based on mean values in the L * component. The 1000 new colours obtained through clustering of pixel values in the R, G and B component are used to construct three different skin lesion models. The validation of the three models is done by comparing the mean values of constructed models and original image, which are found to be the same. For segmentation of skin into involved and non-involved regions, iterative thresholding and Otsu's method are applied in 3 colour spaces, namely, I 1hh. CIE L * a* b* and HSI. The average segmentation errors in the 3 colour spaces are then compared to select the best colour channel in which to perform the segmentation for either thresholding. The specificity and sensitivity analysis with the accompanying Type I error and Type II error are conducted as well. From the segmentation results of skin lesion models, it is found that segmentation in the h colour channel (for fair skin tone), !3 and b colour channels (for middle skin tone) and lz, b and S colour channels (for dark skin tone) yields high accuracy. The same thresholding method in corresponding colour channels is then applied on 20 real skin samples. The segmented images are compared with the reference images to measure the accuracy of the proposed lesion segmentation method in different colour channels. Out of 20 cases, the segmentation method achieved accuracies of higher than 95% for 19 cases. The lowest accuracy obtained is for a particular skin-lesion patch with accuracies of 92 - 93%. The lower accuracy is due to wrinkled skin areas which have been exposed to unequally distributed light leading to misclassification as lesions. For each different skin tone, the overall accuracy results show that the proposed colour channels are appropriate and accurate to carry out the Otsu' s method for segmentation of psoriasis lesions

Objective Assessment of Area and Erythema of Psoriasis Lesion Using Digital Imaging and Colourimetry

Psoriasis is a non-contagious skin disease which typically consists of red plaques covered by silvery-white scales. It affects about 3% of world population. During treatment, dermatologists monitor the extent of psoriasis continuously to ascertain treatment efficacy. Psoriasis Area and Severity Index (PAS!) is the current gold standard method used to assess the extent of psoriasis. In PAS!, there are four parameters to be scored i.e., the surface area affected, erythema (redness), thickness and scaliness of the plaques. Determining PAS! score is a tedious task and thus it is not used in daily clinical practice. In addition, the PAS! parameters are visually determined and may result in intra-observer and inter-observer variations, even by experienced dermatologists. Objective methods in assessing area and erythema of psoriasis lesion have been developed in this thesis. Psoriasis lesion can be recognized by its colour dissimilarity with normal skin. Colour dissimilarity is represented by colour difference in CIELAB colour space, a widely used colour space to measure colour dissimilarity. Each pixel in CIELAB colour space can be represented by its lightness (L'), hue (hob), and chroma (Cab). Colour difference between psoriasis lesion and normal skin is analyzed in hue-chroma plane of CIELAB colour space. Centroids of normal skin and lesion in hue-chroma space are obtained from selected samples. Euclidean distances between all pixels with these two centroids are then calculated. Each pixel is assigned to the class of the nearest centroid. The erythema of psoriasis lesion is affected by degree of severity and skin pigmentation. In order to assess the erythema objectively, patients are grouped according to their skin pigmentation level. The L* value of normal skin which represents skin pigmentation level is utilized to group the patient into the three skin types namely fair, brown and dark skin types. Light difference (t.L*), hue difference (t.hab), and chroma difference (t.C'ab) of CIELAB colour space between reference lesions and the surrounding normal skin are analyzed. It is found that the erythema score of a lesion can be determined by their hue difference (t.hab) value within a particular skin type group. Out of 30 body regions, the proposed method is able to give the same PAS! area score as reference for 28 body regions. The proposed method is able to determine PAS! erythema score of 82 lesions obtained from 22 patients objectively without being influenced by other characteristic of the lesion such as area, pattern, and boundary

AUTOMATED SCORING FOR SCALINESS OF PSORIASIS LESIONS USING EDGE DETECTION

Skin diseases affect 20-30% of the population at any one time, interfering with activities in 10%. Psoriasis, an Mamniatory skin condition and currently incurable is one of the most common skin diseases. About 80% of people who develop psoriasis have plaque psoriasis, which appears as patches of raised, reddish skiii covered by silvery-white scale. The Psoriasis Area and Severity Index (PASI) is the most widely Used tool to assess psoriasis disease severity in clinical trials, although it can be exceedingly cumbersome for use in daily clinical practice. It is proven to be extremely effective in assessing Psoriasis. When Using the PASI, psoriatic plaques are graded based on three criteria: redness, thickness, and scaliness. For the time being, the PASI-scoring are subjective since the assessments are done Visually by the dermatologist. The assessment will result in inter-individual variation between estimates due to different level of experiences and visual acuity. The aim of this project is to develop an automated scoring for scaliness of Psoriasis lesions program Using MATLAB. This project will be Using 2-D Psoriasis images obtained from General Hospital, Kuala Lumpur and medical database online. The MATLAB software will be Used to develop algorithms that are capable to read images of Psoriasis and grade the scaliness scores using the PASI-score texture analysis. The targeted system will include subsystem for acquiring the images, image processing, segmentation, texture analysis for scaliness score and severity based on PASI system

AUTOMATED SCORING FOR SCALINESS OF PSORIASIS LESIONS USING CLUSTER SEGMENTATION

Psoriasis is a chronic skin disease and is a genetically determined inflammatory and proliferate disease, measure by three different parameter; Redness, Scaling and Thickness. Psoriasis is categorized as localized or generalized, based on the severity of the disease and its overall impact on the patient's quality of life and well-being. The most widely method used in clinical trials to evaluate psoriasis disease severity is Psoriasis Area and Severity Index (PASI). The objective of this project is to generate a quantitative score of scaliness for psoriasis disease which to be used in PASI score calculation. The current PASI scoring system is complicated to be used for scaling evaluation. Moreover, psoriasis lesions are quite visible and therefore relatively hard to quantify with human vision. The images used for the analysis are obtained from the dermatologist and psoriasis website, also from General Hospital Ipoh. Throughout this project, MATLAB 7 software is used in obtaining the results. The algorithm is developed together with a sub-system for image acquisition and processing, segmentation, texture analysis for scaliness scores and PASI scoring based on the scales

A COMPUTERIZED DESIGN OF PASI SCORING FOR REDNESS OF PSORIASIS SKIN DISEASE

Psoriasis is one type of skin disorder that is chronic inflammatory skin condition, characterized by localized, widespread well-demarcated red plaques often topped by silvery scales. Dermatologists are using Psoriasis Area and Severity Index (PASI) as a gold standard for evaluating level ofpsoriasis. Four basic characteristics of psoriasis lesion must be calculated for giving PASI score, one of them is lesion redness. The objective of this project is to design and build a computer-based system to enable system to detect lesion region and generate its PASI scoring for redness using image processing technique. The system would assist dermatologists to give the most suitable treatment to the different levels of psoriasis severity based on the redness score using PASI. The sample images are analyzed to classify the severity of the lesion based on color, area covered, shape, size and other features by applying engineering knowledge using the Digital Image Processing Tools in MATLAB7 software. Segmenting lesion from healthy skin is central part of this system. Several filtering and image processing techniques available in the MATLAB7 tools are applied to the sample images to produce their histograms and color distribution particularly in the region concerned. From the results, it shows that the technique used has potential to analyze the sample images. The accuracy of the system can be improved by applying different image processing technique. Result of this project can be analyzed further and served as identification of diagnosis to aid dermatologist in their work. The system is still open for further improvement to increase the accuracy and reliability

An Intelligent Computer-Aided Scheme for Classifying Multiple Skin Lesions

Skin diseases cases are increasing on a daily basis and are difficult to handle due to the global imbalance between skin disease patients and dermatologists. Skin diseases are among the top 5 leading cause of the worldwide disease burden. To reduce this burden, computer-aided diagnosis systems (CAD) are highly demanded. Single disease classification is the major shortcoming in the existing work. Due to the similar characteristics of skin diseases, classification of multiple skin lesions is very challenging. This research work is an extension of our existing work where a novel classification scheme is proposed for multi-class classification. The proposed classification framework can classify an input skin image into one of the six non-overlapping classes i.e., healthy, acne, eczema, psoriasis, benign and malignant melanoma. The proposed classification framework constitutes four steps, i.e., pre-processing, segmentation, feature extraction and classification. Different image processing and machine learning techniques are used to accomplish each step. 10-fold cross-validation is utilized, and experiments are performed on 1800 images. An accuracy of 94.74% was achieved using Quadratic Support Vector Machine. The proposed classification scheme can help patients in the early classification of skin lesions.</p

Psoriasis Skin Disease Classification based on Clinical Images

Psoriasis is an autoimmune skin disorder that causes skin plaques to develop into red and scaly patches. It affects millions of people globally. Dermatologists currently employ visual and haptic methods to determine a medical issue's severity. Intelligent medical imaging-based diagnosis systems are now a possibility because of the relatively recent development of deep learning technologies for medical image processing. These systems can help a human expert make better decisions about a patient's health. Convolutional neural networks, or CNNs, on the other hand, have achieved imaging performance levels comparable to, if not better than, those of humans. In the paper, a Dermnet dataset is used. Image preprocessing, fuzzy c-mean-based segmentation, MobileNet-based feature extraction, and a support vector machine (SVM) classification are used for skin disease classification. Dermnet's dataset was investigated for images of skin conditions using three classes Psoriasis, Dermatofibroma, and Melanoma are studied. The performance metrics such as accuracy, precision-recall, and f1-score are evaluated and compared for three classes of skin diseases. Despite working with a smaller dataset, MobileNet with Support Vector Machine outperforms ResNet in terms of accuracy (99.12%), precision (98.65%), and recall (99.66%)

Adopting Microsoft Excel for Biomedical Signal and Image Processing

Microsoft Excel was recently added to the list of software applications for signal and image processing. The use of Excel as a powerful tool for teaching signal and image data processing techniques as demonstrated in agriculture and natural resource management can be easily adopted for biomedical applications. In the same vein, Excel’s proven utility as a research tool can also be harnessed. This chapter expands the methodology of signal and image formation, visualization, enhancement, and image data fusion using Excel. Different types of techniques used in biomedical imaging are introduced, including: X-ray radiography (X-rays), computerized tomography (CT), ultrasound (U/S), magnetic resonance imaging (MRI), and optical imaging. However, the chapter mainly focuses on optical imaging involving a single spectrum or multiple spectra such as RGB. Specific illustrations of corresponding outputs from different techniques are discussed in the chapter for a better appreciation by the reader

Dermatological diagnosis by mobile application

Health care mobile application delivers the right information at the right time and place to benefit patient’s clinicians and managers to make correct and accurate decisions in health care fields, safer care and less waste, errors, delays and duplicated errors.Lots of people have knowledge a skin illness at some point of their life, For the reason that skin is the body's major organ and it is quite exposed, significantly increasing its hazard of starting to be diseased or ruined.This paper aims to detect skin disease by mobile app using android platform providing valid trustworthy and useful dermatological information on over 4 skin diseases such as acne, psoriasis content for each skin condition, skin rush and Melanoma. It will include name, image, description, symptoms, treatment and prevention with support multi languages English and Bahasa and Mandarin. the application  has the ability to take and send video as well as normal and magnified photos to your dermatologist as an email attachment with comments on safe secure network, this app also has a built in protected privacy features to access to your photo and video dermatologists. The mobile application help in diagnose and treat their patients without an office visit teledermatology is recognized by all major insurance companies doctor.

AN IMAGE BASED SYSTEM TO OBJECTIVEI,Y SCORE THE DEGREE OF REDNESS IN PSORIASIS LESIONS

Nowadays, many skin diseases exist, ranging from hannless such as benign tumors to highly cancerous ones such as malignant melanoma. The visual resemblance of skin lesions requires experienced dennatologists for diagnosis and treatment of skin diseases. One of the most common types of the skin diseases is psoriasis which is chronic inflammatory skin condition, characterized by localized, widespread welldemarcated red plaques often topped by silvery scales. The basic characteristics of psoriasis lesions namely redness, thickness, and scaliness provide a mean of assessing the severity of psoriasis. Dennatologists are using Psoriasis Area and Severity Index (P ASI) score, which takes into account signs such as redness, plaque thickness and scaling in order to assess psoriasis disease severity. The objective of this project is to generate the score of the redness and score of the area covered by psoriasis in order to build automated imaging system capable of classifYing the severity of the disease. This system would assist dennatologists to give the suitable treatment to the different levels of psoriasis severity based on the PASI score. The psoriasis lesion images will be analyzed to classifY the severity based on color, shape, size, and other features by using the Digital Image Processing Tools in MATLAB7 soflware. The entire infonnation obtained through the computer vision and image processing as well as MATLAB7 soflware is applied towards the development of this project. The project will be implemented in two stages .. The first stage (semester 1) involves literature review, research, data gathering, learning and training of the soflware or program and the second stage (semester 2) is analysis of core features, design, testing and analysis of results