Color image segmentation using saturated RGB colors and decoupling the intensity from the hue

Although the RGB space is accepted to represent colors, it is not adequate for color processing. In related works the colors are usually mapped to other color spaces more suitable for color processing, but it may imply an important computational load because of the non-linear operations involved to map the colors between spaces; nevertheless, it is common to find in the state-of-the-art works using the RGB space. In this paper we introduce an approach for color image segmentation, using the RGB space to represent and process colors; where the chromaticity and the intensity are processed separately, mimicking the human perception of color, reducing the underlying sensitiveness to intensity of the RGB space. We show the hue of colors can be processed by training a self-organizing map with chromaticity samples of the most saturated colors, where the training set is small but very representative; once the neural network is trained it can be employed to process any given image without training it again. We create an intensity channel by extracting the magnitudes of the color vectors; by using the Otsu method, we compute the threshold values to divide the intensity range in three classes. We perform experiments with the Berkeley segmentation database; in order to show the benefits of our proposal, we perform experiments with a neural network trained with different colors by subsampling the RGB space, where the chromaticity and the intensity are processed jointly. We evaluate and compare quantitatively the segmented images obtained with both approaches. We claim to obtain competitive results with respect to related works

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

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Segmentation of images by color features: a survey

En este articulo se hace la revisión del estado del arte sobre la segmentación de imagenes de colorImage segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown

Hyperspectral image analysis for questioned historical documents.

This thesis describes the application of spectroscopy and hyperspectral image processing to examine historical manuscripts and text. Major activities in palaeographic and manuscript studies include the recovery of illegible or deleted text, the minute analyses of scribal hands, the identification of inks and the segmentation and dating of text. This thesis describes how Hyperspectral Imaging (HSI), applied in a novel manner, can be used to perform quality text recovery, segmentation and dating of historical documents. The non-destructive optical imaging process of Spectroscopy is described in detail and how it can be used to assist historians and document experts in the exemption of aged manuscripts. This non-destructive optical method of analysis can distinguish subtle differences in the reflectance properties of the materials under study. Many historically significant documents from libraries such as the Royal Irish Academy and the Russell Library at the National University of Ireland, Maynooth, have been the selected for study using the hyperspectral imaging technique. Processing techniques have are described for the applications to the study of manuscripts in a poor state of conservation. The research provides a comprehensive overview of Hyperspectral Imaging (HSI) and associated statistical and analytical methods, and also an in-depth investigation of the practical implementation of such methods to aid document analysts. Specifically, we provide results from employing statistical analytical methods including principal component analysis (PCA), independent component analysis (ICA) and both supervised and automatic clustering methods to historically significant manuscripts and text VIII such as Leabhar na hUidhre, a 12th century Irish text which was subject to part-erasure and rewriting, a 16th Century pastedown cover, and a multi-ink example typical of that found in, for example, late medieval administrative texts such as Gttingen’s kundige bok. The purpose of which is to achieve an overall greater insight into the historical context of the document, which includes the recovery or enhancement of faded or illegible text or text lost through fading, staining, overwriting or other forms of erasure. In addition, we demonstrate prospect of distinguishing different ink-types, and furnishing us with details of the manuscript’s composition, all of which are refinements, which can be used to answer questions about date and provenance. This process marks a new departure for the study of manuscripts and may provide answer many long-standing questions posed by palaeographers and by scholars in a variety of disciplines. Furthermore, through text retrieval, it holds out the prospect of adding considerably to the existing corpus of texts and to providing very many new research opportunities for coming generations of scholars

Histological Quantification in Temporal Lobe Epilepsy

Approximately 30 percent of epilepsy patients suffer from refractory temporal lobe epilepsy which is commonly treated with resection of the epileptogenic tissue. However, surgical treatment presents many challenges in locating the epileptogenic focus and thus not all patients become seizure-free following surgery. Advances in techniques can lead to improved localization of the epileptogenic zone and may be validated by correlating MRI with neuropathology of the excised cortical tissue. Focal cortical dysplasias are a neuropathological group of cortical malformations that are often found in cases of refractory epilepsy, however, they are subtle and difficult to quantify. The purpose of this research is to employ histology image analysis techniques to better characterize these abnormalities at the neuronal and laminar level, allowing for correlative MRI-histology studies and improved lesion detection in medically intractable TLE