ANALIZA OBSZARÓW ZMIAN SKÓRNYCH PO SEGMENTACJI PRZEZ PROGOWANIE

Melanoma is one of the fastest spreading cancers. The aim of the article is to segment the skin lesions from human skin dermatoscopic images covered by melanoma. Threshold segmentation was used, which allows a single skin lesion to be analyzed. It shows the four areas of each based on their color. The created software monitors the border of skin lesion areas. Segmentation and analysis of the resulting images with different areas of skin change was carried out in the Matlab software.Czerniak to jeden z najszybciej rozprzestrzeniających się nowotworów. Celem artykułu jest segmentacja zmiany skórnej z obrazów dermatoskopowych ludzkiej skóry objętych czerniakiem. Użyto segmentacj przez progowanie, która pozwala na analizę pojedyńczej zmiany skórnej. Ukazuje cztery obszary każdej z nich w oparciu o ich barwę. Stworzone oprogramowanie monitoruje granicę obszarów zmiany skórnej. Segmentacjai analiza powstałych obrazów z różnymi obszarami zmiany skórnej została przeprowadzona w środowisku Matlab

Melanoma Detection Using Mobile Technology and Feature-Based Classification Techniques

Melanoma is one of the most dangerous types of skin cancer in terms of the ratio of death cases. Probability of death increases when it is diagnosed late. However, it is possible to treat melanoma successfully when diagnosed in its early stages. One of the most common medical methods for diagnosing melanoma is the ABCD (Asymmetry, Border irregularity, Color, and Diameter) method that involves the measurement of four features of skin lesions. The main disadvantage of this method is that estimation error and subjectivity affects the accuracy of diagnosis, especially when performed by non-specialists. Scarcity of specialists makes the problem worse. This has led to the development of computer systems to help in melanoma diagnosis. However, while most computer systems can achieve high accuracy with adequate speed, they have problems in the usability and flexibility. The emergence of smart phones with increasing image capture and processing capabilities has made it more possible to use such devices to perform medical image analysis such as the diagnosis of melanoma. Our research work combines existing melanoma diagnosis method and the image capture and processing capabilities of smart phones to achieve fast, affordable, easily available and highly accurate melanoma diagnosis. In this work, we propose a complete smart phone application to capture, and process an image of the suspicious region of the skin in order to estimate its probability of being melanoma. The system can use historical cases to improve its diagnosis accuracy. The system was tested on 164 sample images. 14 images were not well-captured and could not be diagnosed, while the remaining 150 cases were successfully processed. In each of these 150 images, the lesion was correctly segmented and their ABCD feature set extracted. Diagnosis accuracy of the analyzed images ranged between 88%-94 with best results using SVM classifier, and worst is the KNN classifier.الميلانوما أحد أخطر أنواع سرطان الجلد من حيث نسبة عدد الوفيات الى حالات الاصابة ،تزداد الخطورة في الحالات التي يتم معالجتها في مراحل متأخرة ، ولكن يمكن علاج الميلانوما بنجاح اذا تم اكتشاف المرض في مراحله الاولى ، لذلك هناك العديد من الطرق المتبعة لتشخيص المرض في المراحل الأولى لتوجيه المريض الى الطبيب المختص مباشرة في حالة الشك في وجود المرض ، اشهر هذه الطرق هي طريقة ABCD، وأهم العقبات التي تواجه هذه الطريقة هي عدم دقة التنفيذ من الاشخاص غير المتخصصين ، لأن اعتماد الطريقة على خصائص مثل الحجم واللون والشكل يجعلها عرضة للكثير من التقدير والنسبية في التشخيص مما يفقد هذه الطريقة الكثير من الدقة في النتائج ، لذلك تم خلال الاعوام السابقة العمل على العديد من الانظمة المحوسبة التي تسعي الى المساعدة في التشخيص وتقليل نسبة الخطأ، وأتمتة التشخيص بحيث لا يخضع للتقدير الشخصي ، ومع بداية ظهور الاجهزة الذكية والتي تجاوز استخدامها حدود التواصل ليتم استخدامها في التقاط الصور بدقة عالية ومعالجة البيانات بكفاءة والتواصل مع شبكة الانترنت، كان اتجاه للاستفادة من مرونة الأجهزة الذكية ودقتها في التقاط الصور ومعالجتها من اجل توفير انظمة لتشخيص الأمراض المختلفة، وكان نصيب من هذه الابحاث والتطبيقات لتسهيل عملية تشخيص الميلانوما، ورغم توفر العديد من الأنظمة والابحاث الا أن النتائج لا تزال في المراحل الاولى حيث هناك العديد من العقبات، فلا تزال دقة هذه الانظمة لا تصل الى المستوى المطلوب مما يجعلها في بعض الحالات خطر على حياة المريض اذا تم تشخيص الحالات المصابة بشكل سلبي، كما ان امكانات معالجة الصور وتخزين البيانات وتصنيفها يعتبر من المجالات الجديدة والتي لم يتم اختبارها بشكل كافي في الابحاث السابقة ولم يتم التعامل مع الامكانات الجديدة للهواتف الذكية ، وخلال فترة قريبة كان مثلا تحليل كميات كبيرة من البيانات على الهواتف الذكية ومعالجتها وتصنيفها من الامور الغير ممكنة والتي اصبحت الان من الامور الممكنة في ظل تطور وحدات التخزين والمعالجة بشكل كبير للهواتف الذكية. لذلك جاء هذا العمل لمواصلة الجهد المبذول في توفير حل لمشكلة تشخيص مرض الميلانوما بشكل دقيق وفعال ومرن باستخدام الهواتف الذكية ، حيث تم استخدام امكانات الهواتف الذكية في التقاط الصور ومعالجتها بالإضافة لإمكانية تخزين المعلومات واستخدامها في التصنيف وتوقع الحالات الجديدة المصابة بالمرض، وتم بناء واختبار نظام كامل لتحقيق ذلك ، وقد كانت نتائج العمل مُرضِية جدا حيث تم فحص البرنامج على عينة مكونة من 164 صورة حيث نجح البرنامج في مرحلة معالجة الصورة في معالجة 150 صورة وعزل منطقة الآفة من أصل 164 صورة كما ذكرنا، علما ان نسبة النجاح في معالجة الصور تم تحسينها لتجاوز الأخطاء اثناء التقاط الصور من خلال استخدام واجهة تفاعلية للمستخدم ، اما في مرحلة التصنيف علي 150 صورة الناتجة من معالجة الصور فكانت دقة نتائج التصنيف بين 88-94% حسب نظام التصنيف المتبع ، وهي نتائج جيدة و يمكن البناء عليها في استخدام الموبايل في التشخيص الأولى لمرضى الميلانوم

Semi-automated techniques for the retrieval of dermatological condition in color skin images

Dermatologists base the diagnosis of skin disease on the visual assessment of the skin. This fact shows that correct diagnosis is highly dependent on the observer\u27s experience and on his or her visual perception. Moreover, the human vision system lacks accuracy, reproducibility, and quantification in the way it gathers information from an image. So, there is a great need for computer-aided diagnosis. We propose a content-based image retrieval (CBIR) system to aid in the diagnosis of skin disease. First, after examining the skin images, pre-processing will be performed. Second, we examine the visual features for skin disease classified in the database and select color, texture and shape for characterization of a certain skin disease. Third, feature extraction techniques for each visual feature are investigated respectively. Fourth, similarity measures based on the extracted features will be discussed. Last, after discussing single feature performance, a distance metric combination scheme will be explored. The experimental data set is divided into two parts: developmental data set used as an image library and an unlabeled independent test data set. Two sets of experiments are performed: the input image of the skin image retrieval algorithm is either from developmental data set or independent test data set. The results are top five candidates of the input query image, that is, five labeled images from image library. Results are laid out separately for developmental data set and independent test data set. Two evaluation systems, both the standard precision vs. recall method, and the self-developed scoring method are carried out. The evaluation results obtained by both methods are given for each class of disease. Among all visual features, we found the color feature played a dominating role in distinguishing different types of skin disease. Among all classes of images, the class with best feature consistency gained the best retrieval accuracy based on the evaluation result. For future research we recommend further work in image collection protocol, color balancing, combining the feature metrics, improving texture characterization and incorporating semantic assistance in the retrieved process

Estimación de áreas de heridas de Leishmaniasis por procesamiento digital de imágenes y su correlación con la evolución al tratamiento

La Leishmaniasis es una enfermedad parasitaria de difícil curación caracterizada por comprometer la piel, mucosas y vísceras. Actualmente, el Grupo de Estudios de Leishmaniasis (GEL) del Instituto de Medicina Tropical Alexander von Humboldt (IMTAvH) de la Universidad Peruana Cayetano Heredia (UPCH) evalúa la mejora del tratamiento de lesiones en la piel ocasionada por la Leishmaniasis cutánea (LC) mediante su categorización en estadíos. Como posible indicador de mejoría los médicos y personal especializado miden el área de la lesión aproximándola a una elipse y trazando el borde de la misma con una lámina transparente sobrepuesta a la superficie de la piel. Estos procedimientos presentan criterios subjetivos para su evaluación, ya que están basados en la experiencia de cada médico. Por tanto, la percepción de mejoría de la lesión presenta alta variabilidad según el médico encargado de la evaluación del tratamiento. En este documento se presenta la descripción de la enfermedad, así como una breve referencia al uso del procesamiento de imágenes aplicado a lesiones de piel y su comparación con métodos manuales. Se muestra la metodología aplicada para el reclutamiento, consentimiento y monitoreo de los pacientes y la metodología para la medición manual y automática. Se describe el proceso de adquisición de imágenes, los procedimientos para hallar el área en las mediciones manuales y las etapas del algoritmo automático para clasificar la lesión y estimar su área. Se indica la evolución del área de la lesión durante el tratamiento en distintos pacientes y se compara con los métodos manuales. Se calculó la variabilidad existente, se obtuvo resultados de hasta un 40% de variabilidad en mediciones manuales, una variabilidad alta para determinar con certeza el valor del área medida. Se mejoró el método de adquisición de imágenes propuesto por el Laboratorio de Imágenes Médicas-PUCP en una investigación previa (Ej. condiciones de iluminación). El algoritmo automático logra estimar el área de manera repetible y este puede ser comparable con los métodos manuales. De esta manera, se corrobora la factibilidad de uso de técnicas de procesamiento de imágenes para obtener el área de la lesión. En los pacientes que dieron su consentimiento para esta investigación, se halló el porcentaje de disminución del área de la lesión respecto al valor inicial, de esta forma, es factible ubicar la lesión en uno de los cinco estadíos propuestos por el GEL-UPCH. Finalmente, se recomienda seguir con la presente investigación haciendo el seguimiento a nuevos pacientes que presenten LC al menos al 95% del tiempo de tratamiento para lograr una validación médica. El área de piel infiltrada no pudo ser estimada debido a que su clasificación depende de la textura de la piel y no del color. Por tanto, se propone y recomienda utilizar alguna técnica de ultrasonido que pueda estimar la rigidez de la piel enferma, de esta manera, se pueda estimar el área para piel infiltrada.Tesi

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

Markov-Gibbs Random Field Approach for Modeling of Skin Surface Textures

Medical imaging has been contributing to dermatology by providing computer-based assistance by 2D digital imaging of skin and processing of images. Skin imaging can be more effective by inclusion of 3D skin features. Furthermore, clinical examination of skin consists of both visual and tactile inspection. The tactile sensation is related to 3D surface profiles and mechanical parameters. The 3D imaging of skin can also be integrated with haptic technology for computer-based tactile inspection. The research objective of this work is to model 3D surface textures of skin. A 3D image acquisition set up capturing skin surface textures at high resolution (~0.1 mm) has been used. An algorithm to extract 2D grayscale texture (height map) from 3D texture has been presented. The extracted 2D textures are then modeled using Markov-Gibbs random field (MGRF) modeling technique. The modeling results for MGRF model depend on input texture characteristics. The homogeneous, spatially invariant texture patterns are modeled successfully. From the observation of skin samples, we classify three key features of3D skin profiles i.e. curvature of underlying limb, wrinkles/line like features and fine textures. The skin samples are distributed in three input sets to see the MGRF model's response to each of these 3D features. First set consists of all three features. Second set is obtained after elimination of curvature and contains both wrinkle/line like features and fine textures. Third set is also obtained after elimination of curvature but consists of fine textures only. MGRF modeling for set I did not result in any visual similarity. Hence the curvature of underlying limbs cannot be modeled successfully and makes an inhomogeneous feature. For set 2 the wrinkle/line like features can be modeled with low/medium visual similarity depending on the spatial invariance. The results for set 3 show that fine textures of skin are almost always modeled successfully with medium/high visual similarity and make a homogeneous feature. We conclude that the MGRF model is able to model fine textures of skin successfully which are on scale of~ 0.1 mm. The surface profiles on this resolution can provide haptic sensation of roughness and friction. Therefore fine textures can be an important clue to different skin conditions perceived through tactile inspection via a haptic device