Hacia el modelado 3d de tumores cerebrales mediante endoneurosonografía y redes neuronales

Las cirugías mínimamente invasivas se han vuelto populares debido a que implican menos riesgos con respecto a las intervenciones tradicionales. En neurocirugía, las tendencias recientes sugieren el uso conjunto de la endoscopia y el ultrasonido, técnica llamada endoneurosonografía (ENS), para la virtualización 3D de las estructuras del cerebro en tiempo real. La información ENS se puede utilizar para generar modelos 3D de los tumores del cerebro durante la cirugía. En este trabajo, presentamos una metodología para el modelado 3D de tumores cerebrales con ENS y redes neuronales. Específicamente, se estudió el uso de mapas auto-organizados (SOM) y de redes neuronales tipo gas (NGN). En comparación con otras técnicas, el modelado 3D usando redes neuronales ofrece ventajas debido a que la morfología del tumor se codifica directamente sobre los pesos sinápticos de la red, no requiere ningún conocimiento a priori y la representación puede ser desarrollada en dos etapas: entrenamiento fuera de línea y adaptación en línea. Se realizan pruebas experimentales con maniquíes médicos de tumores cerebrales. Al final del documento, se presentan los resultados del modelado 3D a partir de una base de datos ENS.Minimally invasive surgeries have become popular because they reduce the typical risks of traditional interventions. In neurosurgery, recent trends suggest the combined use of endoscopy and ultrasound (endoneurosonography or ENS) for 3D virtualization of brain structures in real time. The ENS information can be used to generate 3D models of brain tumors during a surgery. This paper introduces a methodology for 3D modeling of brain tumors using ENS and unsupervised neural networks. The use of self-organizing maps (SOM) and neural gas networks (NGN) is particularly studied. Compared to other techniques, 3D modeling using neural networks offers advantages, since tumor morphology is directly encoded in synaptic weights of the network, no a priori knowledge is required, and the representation can be developed in two stages: off-line training and on-line adaptation. Experimental tests were performed using virtualized phantom brain tumors. At the end of the paper, the results of 3D modeling from an ENS database are presented

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Perceptual-based textures for scene labeling: a bottom-up and a top-down approach

Due to the semantic gap, the automatic interpretation of digital images is a very challenging task. Both the segmentation and classification are intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired texture features for material classification and interpreting outdoor scenery images. Experiments show that the presented texture features obtain the best classification results for material recognition compared to other well-known texture features, with an average classification rate of 93.0%. For scene analysis, both a bottom-up and top-down strategy are employed to bridge the semantic gap. At first, images are segmented into regions based on the perceptual texture and next, a semantic label is calculated for these regions. Since this emerging interpretation is still error prone, domain knowledge is ingested to achieve a more accurate description of the depicted scene. By applying both strategies, 91.9% of the pixels from outdoor scenery images obtained a correct label

Satellite-based precipitation estimation using watershed segmentation and growing hierarchical self-organizing map

This paper outlines the development of a multi-satellite precipitation estimation methodology that draws on techniques from machine learning and morphology to produce high-resolution, short-duration rainfall estimates in an automated fashion. First, cloud systems are identified from geostationary infrared imagery using morphology based watershed segmentation algorithm. Second, a novel pattern recognition technique, growing hierarchical self-organizing map (GHSOM), is used to classify clouds into a number of clusters with hierarchical architecture. Finally, each cloud cluster is associated with co-registered passive microwave rainfall observations through a cumulative histogram matching approach. The network was initially trained using remotely sensed geostationary infrared satellite imagery and hourly ground-radar data in lieu of a dense constellation of polar-orbiting spacecraft such as the proposed global precipitation measurement (GPM) mission. Ground-radar and gauge rainfall measurements were used to evaluate this technique for both warm (June 2004) and cold seasons (December 2004-February 2005) at various temporal (daily and monthly) and spatial (0.04 and 0.25) scales. Significant improvements of estimation accuracy are found classifying the clouds into hierarchical sub-layers rather than a single layer. Furthermore, 2-year (2003-2004) satellite rainfall estimates generated by the current algorithm were compared with gauge-corrected Stage IV radar rainfall at various time scales over continental United States. This study demonstrates the usefulness of the watershed segmentation and the GHSOM in satellite-based rainfall estimations

Segmentasi mental foramen di mandibula pada citra radiografi panoramik dengan Self-Organizing Map

Sistem berbasis komputer di bidang medis dapat digunakan untuk membantu mendiagnosis penyakit tertentu. Pembuatan sistem berbasis komputer berdasarkan citra mempunyai beberapa tahapan penting, diantaranya adalah tahapan segmentasi. Tahapan segmentasi merupakan tahapan untuk melakukan pemisahan objek terhadap background. Thresholding merupakan metode dalam melakukan segmentasi, di mana prosesnya didasarkan pada warna keabuan yang menghasilkan citra biner; 1 (putih) untuk mewakili objek dan 0 (hitam) untuk mewakili background. Mental foramen adalah bagian yang ada dalam mandibula, salah satu fungsinya untuk identifikasi forensik. Agar fungsi mental foramen bisa digunakan, maka salah satu proses yang harus dilalui adalah proses segmentasi. Tujuan penelitian ini adalah melakukan segmentasi mental foramen di mandibula citra radiografi gigi. Manfaat dari melakukan segmentasi mental foramen pada mandibula adalah dapat menampilkan informasi mental foramen di mandibula secara jelas pada citra radiografi gigi agar dapat digunakan pada proses identifikasi manusia di kedokteran forensik gigi. Adapun algoritma yang digunakan dalam melakukan thresholding adalah Self-Organizing Map (SOM), karena telah terbukti dapat melakukan segmentasi lebih baik. Tahapan penelitian ini terdiri dari 1) Pengumpulan citra radiografi panoramic didapatkan dari RSUD Ibnu Sina Gresik di Jawa Timur sebanyak 16 citra radiografi panoramic. 2)  Citra radiografi panoramic dilakukan akuisisi agar menghasilkan citra digital; 3) Perbaikan citra menggunakan ekualisasi histogram; dan 4) Pengambilan bagian mental foramen di mandibula terlebih dahulu dilakukan croping menggunakan SOM agar komputasi tidak tinggi. Berdasarkan hasil uji coba, SOM memiliki kinerja kurang bagus dalam melakukan segmentasi mental foramen pada mandibula secara sempurna, karena hanya mampu melakukan segmentasi secara baik sebanyak 3 citra dari 16 citra berdasarkan pengamatan langsung secara manual.  Computer based system in the medical field is used to assist a diagnose of certain diseases. There are several steps to process a digital image, and the necessary part of it is image segmentation. Image segmentation is applied for separating between the foreground and background of an image. Image thresholding is a basic image segmentation that produces a binary image from a gray-level image, which 1 represents as an object; otherwise, it is the background. Mental foramina is a part of the mandibular canal that is used to acknowledge of digital forensics. In this paper, we apply mental foramina image segmentation on the mandible canal in dental radiographic. The use of mental foramina segmentation is to perform its information on mandibular obviously so that it can be used for human identification in the medical of dental graphics. We utilize the Self-Organizing Map (SOM) as it has better segmentation than other algorithms. In research methodology, we divide the process as follows: 1) primary dataset of panoramic radiographic images was obtained from RSUD Ibnu Sina Gresik, East Java with the total of images is 16. 2) The acquisition of panoramic radiographic images into digital images. 3) Image enhancement using histogram equalization. 4) Mental foramina images on the mandibular canal were cropped using SOM to avoid a high computational process. The result shows that SOM achieves low evaluation of metal foramina image segmentation on the mandibular canal since it is only undertaking three out of sixteen images based on visualization measurement