Software for automatic diagnostic prediction of skin clinical images based on ABCD rule / Software para predição diagnóstica automática de imagens clínicas da pele baseada na regra ABCD

Cancer is responsible for about 7 million annual deaths worldwide. Among them, the melanoma type, responsible for 4% of the skin cancers, whose incidence has doubled in the last ten years. The processing of digital images has shown good potential for assistance in the early detection of melanomas. In this sense, the objective of the current study was to develop a software for clinical images processing and reach a score of accuracy higher than 95%. The ABCD rule was used as a guide for the development of computational analysis methods. MATLAB was used as programming environment for the development of the processing of digital images software. The images used were acquired from two banks of free images. They included images of melanomas (n=15) and nevi images (not cancer) (n=15). Images in RGB color channel were used, which were converted to grayscale, 8x8 median filter applications and 3x3 neighborhood approach technique. After, we proceeded to the binarization and inversion of black and white for later extraction of contour characteristics of the lesion. The classifier used was an artificial neural network of radial basis, getting accuracy for diagnosis of melanomas images of 100% and of 90.9% for not cancer images. Thus, global correction for diagnostic prediction was 95.5%. An area under the ROC graph 0.967 was achieved, suggesting a great diagnostic predictive ability. Besides, the software presents low cost use, since it can be run on most operating systems used nowadays.

O câncer é responsável por cerca de 7 milhões de óbitos anuais em todo o mundo. Entre eles, o tipo melanoma, responsável por 4% dos cânceres de pele, cuja incidência dobrou mundialmente nos últimos dez anos. O processamento digital de imagens tem mostrado um bom potencial para auxiliar no diagnóstico precoce de melanomas. Neste sentido, objetivo do presente estudo foi desenvolver um software para processamento digital de imagens clínicas para diagnóstico automático baseado na regra ABCD que alcançasse um percentual de acerto maior do que 95% dos casos. Utilizou-se como norteador a regra ABCD para o desenvolvimento de métodos de análise computacional. Empregou-se o MATLAB como ambiente de programação para o desenvolvimento de um software para o processamento digital de imagens. As imagens utilizadas foram adquiridas de dois bancos de imagens de acesso livre. Foram inclusas imagens clínicas de melanomas (n=15) e imagens de nevos (lesão melanocítica benigna) (n=15). Utilizaram-se imagens no canal de cor RGB, as quais foram convertidas para escala de cinza, aplicação de filtro de mediana 8x8 e técnica de aproximação por vizinhança 3x3. Após, procedeu-se a binarização e inversão de preto e branco para posterior extração das características do contorno da lesão. O classificador utilizado foi uma rede neural artificial de base radial, obtendo acerto diagnóstico para as imagens melanomas de 100% e para imagens benignas de 90,9%. Desta forma, o acerto global para predição diagnóstica foi de 95,5%. Obteve-se uma área sob a curva ROC de 0,967, o que sugere uma excelente capacidade de predição diagnóstica, sobretudo, com baixo custo de utilização, visto que o software pode ser executado na grande maioria dos sistemas operacionais hoje utilizados.

3D thermal medical image visualization tool: Integration between MRI and thermographic images.

Three-dimensional medical image reconstruction using different images modalities require registration techniques that are, in general, based on the stacking of 2D MRI/CT images slices. In this way, the integration of two different imaging modalities: anatomical (MRI/CT) and physiological information (infrared image), to generate a 3D thermal model, is a new methodology still under development. This paper presents a 3D THERMO interface that provides flexibility for the 3D visualization: it incorporates the DICOM parameters; different color scale palettes at the final 3D model; 3D visualization at different planes of sections; and a filtering option that provides better image visualization. To summarize, the 3D thermographc medical image visualization provides a realistic and precise medical tool. The merging of two different imaging modalities allows better quality and more fidelity, especially for medical applications in which the temperature changes are clinically significant.

Image fusion improvements applied at the generation of 3D thermal models.

The application of multimodal image registration to various medical applications has been investigated. Image fusion involving 3D thermal and MRI/CT images allows the extraction of both functional and anatomical information, which may become a powerful tool to aid in clinical diagnoses. This paper presents innovations at the image fusion methodology, which currently requires that both imaging modalities are represented and visualized at the same 3D viewing projection. The proposed solution is based and compared with two different viewing projections: orthogonal and perspective. The methodology requires that the thermographic images (or photographs) are visualized in the orthogonal view, in order to match with the 2D projected images (using range images) from MRI/CT. The results obtained have shown significant improvements in the 3D thermal models, when compared and evaluated with the perspective approach. This allowed the generation of more accurate 3D models, which match both the geometry and texture (functional temperature information). Since it is desirable to combine or unify more than one imaging modality, these 3D multimodal models may have a strong impact in many clinical applications.

Fusão 3D de imagens de MRI/CT e termografia / 3D image fusion using MRI/CT and infrared images

INTRODUÇÃO: A termografia por imagem infravermelha (IR) é uma técnica para diagnóstico não-invasiva que permite a avaliação e quantificação de variações de temperatura na superfície da pele. Apesar de fornecer informações significativas para auxiliar no diagnóstico médico, esta técnica não permite avaliar det alhes anatômicos da região sendo analisada. Este artigo apresenta uma nova metodologia para realizar a fusão entre diferentes modalidades de imagens, tais como ressonância magnética (MRI) ou tomografia computadorizada por raios X (CT), juntamente com imagens de termografia infravermelha. MÉTODOS: Para a construção do modelo 3D, primeiramente são adquiridas as imagens por ressonância magnética (MRI) ou tomografia computadorizada (CT) e um conjunto de imagens térmicas da região de interesse. Em seguida, realiza-se o registro utilizando as projeções 2D (dos planos tomográficos) com as imagens térmicas. Após o registro, as imagens térmicas são combinadas e projetadas sobre o modelo 3D das imagens de MRI ou CT. RESULTADOS: O resultado é uma imagem 3D que combina informação de duas modalidades de imagens médicas diferentes. A combinação dessas duas modalidades de imagens médicas disponibiliza uma nova técnica de imagem 3D que agrupa informações anatômicas (MRI ou CT) e funcionais (variações de temperatura na superfície do corpo). CONCLUSÃO: Os resultados obtidos até o momento com essa nova metodologia indicam que ela pode auxiliar em diagnósticos médicos.

INTRODUCTION: Infrared (IR) thermal imaging is a non-invasive and diagnostic technique that allows evaluation and quantification based on the temperature changes of the skin surface. It provides significant information for clinical diagnosis; however this technique does not present the anatomical details of the region under inspection. In this work, it is presented an innovative image fusion method between different imaging modalities, such as magnetic resonance images (MRI) or X-ray computed tomography (CT), together with IR thermal images. METHODS: Firstly, in order to build the 3D model, the MRI or CT images and the IR thermal images (from the region of interest) are acquired. Then, based on the tomographic planes (image slices), the 2D projections are generated, and the IR images are registered accordingly. Next, the already registered IR set of images are combined and projected over the 3D MRI or CT model. RESULTS: The result is a 3D fused image that combines the information contents from the two different medical imaging modalities. The combination of these two medical imaging modalities offers a new 3D imaging technique that combines anatomical (MRI or CT) and functional (the body's surface temperature) information. CONCLUSION: The results obtained up to now with this new methodology indicate that it can aid in medical diagnosis.

A computer tool for the fusion and visualization of thermal and magnetic resonance images.

The measurement of temperature variation along the surface of the body, provided by digital infrared thermal imaging (DITI), is becoming a valuable auxiliary tool for the early detection of many diseases in medicine. However, DITI is essentially a 2-D technique and its image does not provide useful anatomical information associated with it. However, multimodal image registration and fusion may overcome this difficulty and provide additional information for diagnosis purposes. In this paper, a new method of registering and merging 2-D DITI and 3-D MRI is presented. Registration of the images acquired from the two modalities is necessary as they are acquired with different image systems. Firstly, the body volume of interest is scanned by a MRI system and a set of 2-D DITI of it, at orthogonal angles, is acquired. Next, it is necessary to register these two different sets of images. This is done by creating 2-D MRI projections from the reconstructed 3-D MRI volume and registering it with the DITI. Once registered, the DITI is then projected over the 3-D MRI. The program developed to assess the proposed method to combine MRI and DITI resulted in a new tool for fusing two different image modalities, and it can help medical doctors.