A method for the segmentation of images based on thresholding and applied to vesicular textures

In image processing, a segmentation is a process of partitioning an image into multiple sets of pixels, that are defined as super-pixels. Each super-pixel is characterized by a label or parameter. Here, we are proposing a method for determining the super-pixels based on the thresholding of the image. This approach is quite useful for studying the images showing vesicular textures.Comment: Keywords: Segmentation, Edge Detection, Image Analysis, 2D Textures, Texture Function

A method based on image segmentation for the analysis of the orientation of rod-like objects

Here we show a method of image processing, which can be applied to the measurement of the orientation of rod-like objects. The method is based on an image segmentation recently developed and used for the analysis of micrographs. The segmentation allows to determine specific objects in the image, the orientation of which can be measured with respect to the axes of the image frame. The orientation is determined by means of the elements of a matrix of inertia. Two lengths are associated to the elements of the diagonal of the matrix. Their ratio is giving the arctangent of an angle, between 0 and 90 degrees; the element outside the diagonal is giving the sign of this angle, which is measuring the orientation of segmented rod-like objects

Classification and Segmentation of MRI Brain Images using Support Vector Machine and Fuzzy C-means Clustering

An early diagnosis of brain disorders is very important for timely treatment of such diseases.Several imaging modalities are used to capture the anomalities by obtaining either the  physiological or morphological information. The scans obtained using imaging modalities such as magnetic resonance imaging (MRI) are investigated by the radiologists in order to diagnose the diseases. However such investigations are time consuming and might involve errors. In this paper, a fuzzy c-means clustering method is used for brain MRI image segmentation.The GLCM features are obtained from the segmented images and are subsequently mapped in to a PCA space. A support vector machine (SVM) classifier is used to classify brain MRI images taken from BRATS-13 images. The method is evaluated by employing various performance measures such as  Jaccard index, Dice index, mean square error (MSE), peak signal to noise ratio (PSNR). The results show that the method outperforms the existing methods

Image Segmentation Applied to the Analysis of Fabric Textures

In this work, we are proposing the use of image segmentation to the analysis of the textures of fabrics, with the aim of applying this approach to a fabric fault detection based on image processing

Image Segmentation Applied to the Study of Micrographs of Cellular Solids

The paper is proposing a method of image segmentation applied to the study of the micrographs of cellular solids. The segmentation is based on a thresholding which creates a binary (black and white) image of the micrograph. The binary image is divided in super-pixels which correspond to the microcells of the material. From the areas of the super-pixels it is easy to evaluate the distribution of the size of the cells and correlate this distribution to the properties of the material

Inteligencia computacional aplicada a la segmentación de imágenes de resonancia magnética cerebral para la diagnosis y tratamiento médico

Segmentation of medical magneticresonance images present in most of themethod described will be developed insome kind of methodology or related clustering data design to classifiers models, as an introduction to the basic ideas under lying fuzzy pattern recognition, topological properties inreconstructing anatomical tissue andquality representation such medical images features. This is way and approach involving techniques working with modeling the vagueness oruncertainty particularly, fuzzy clustering models as the tool used on the classification systems combined with anatomical reconstruction model evidencing the pathological tissuei dentification process. Finally, this groupof structured techniques algorithms transfers knowledge of the medical domain for use in the reconstruction of volumetric surfaces retain the anatomy ofthe object of interest, in thiscase thepossibility to locate a tumor or lesion.La segmentación de imágenes médicas de resonancia magnética, presente en los métodos que se describen, y que serán desarrollados con algún tipo de tecnología relacionada a los modelos de clasificación en agrupamiento de datos,están basados en las teorías básicas subyacentes al reconocimiento de patrones difusos, a las propiedades topológicas en la reconstrucción de tejido anatómico y a la calidad de representación de características de la imagen. Así se involucran técnicas de trabajo con el modelado de vaguedad o incertidumbre utilizando modelos de agrupamiento difuso como herramienta principal del sistema de clasificación,combinados con el modelado de reconstrucción anatómica que evidencie el proceso de identificación de tejido patológico. Finalmente, este grupo de técnicas estructuradas en forma de algoritmos, transfiere conocimiento del dominio médico para ser utilizados en la reconstrucción de superficies volumétricas que conserven la anatomía del objeto de interés; en nuestro caso, la posibilidad de localizar o representar un tumor o lesión

Brain Tumor Segmentation Methods based on MRI images: Review Paper

Statistically, incidence rate of brain tumors for women is 26.55 per 100,000 and this rate for men is 22.37 per 100,000 on average. The most dangerous occurring type of these tumors are known as Gliomas. The form of cancerous tumors so-called Glioblastomas are so aggressive that patients between ages 40 to 64 have only a 5.3% chance with a 5-year survival rate. In addition, it mostly depends on treatment course procedures since 331 to 529 is median survival time that shows how this class is commonly severe form of brain cancer. Unfortunately, a mean expenditure of glioblastoma costs 100,000$. Due to high mortality rates, gliomas and glioblastomas should be determined and diagnosed accurately to follow early stages of those cases. However, a method which is suitable to diagnose a course of treatment and screen deterministic features including location, spread and volume is multimodality magnetic resonance imaging for gliomas. The tumor segmentation process is determined through the ability to advance in computer vision. More precisely, CNN (convolutional neural networks) demonstrates stable and effective outcomes similar to other automated methods in terms of tumor segmentation algorithms. However, I will present all methods separately to specify effectiveness and accuracy of segmentation of tumor. Also, most commonly known techniques based on GANs (generative adversarial networks) have an advantage in some domains to analyze nature of manual segmentations.

DAFNet: A dual attention-guided fuzzy network for cardiac MRI segmentation

Background: In clinical diagnostics, magnetic resonance imaging (MRI) technology plays a crucial role in the recognition of cardiac regions, serving as a pivotal tool to assist physicians in diagnosing cardiac diseases. Despite the notable success of convolutional neural networks (CNNs) in cardiac MRI segmentation, it remains a challenge to use existing CNNs-based methods to deal with fuzzy information in cardiac MRI. Therefore, we proposed a novel network architecture named DAFNet to comprehensively address these challenges. Methods: The proposed method was used to design a fuzzy convolutional module, which could improve the feature extraction performance of the network by utilizing fuzzy information that was easily ignored in medical images while retaining the advantage of attention mechanism. Then, a multi-scale feature refinement structure was designed in the decoder portion to solve the problem that the decoder structure of the existing network had poor results in obtaining the final segmentation mask. This structure further improved the performance of the network by aggregating segmentation results from multi-scale feature maps. Additionally, we introduced the dynamic convolution theory, which could further increase the pixel segmentation accuracy of the network. Result: The effectiveness of DAFNet was extensively validated for three datasets. The results demonstrated that the proposed method achieved DSC metrics of 0.942 and 0.885, and HD metricd of 2.50mm and 3.79mm on the first and second dataset, respectively. The recognition accuracy of left ventricular end-diastolic diameter recognition on the third dataset was 98.42%. Conclusion: Compared with the existing CNNs-based methods, the DAFNet achieved state-of-the-art segmentation performance and verified its effectiveness in clinical diagnosis