Advanced Brain Tumour Segmentation from MRI Images

Magnetic resonance imaging (MRI) is widely used medical technology for diagnosis of various tissue abnormalities, detection of tumors. The active development in the computerized medical image segmentation has played a vital role in scientific research. This helps the doctors to take necessary treatment in an easy manner with fast decision making. Brain tumor segmentation is a hot point in the research field of Information technology with biomedical engineering. The brain tumor segmentation is motivated by assessing tumor growth, treatment responses, computer-based surgery, treatment of radiation therapy, and developing tumor growth models. Therefore, computer-aided diagnostic system is meaningful in medical treatments to reducing the workload of doctors and giving the accurate results. This chapter explains the causes, awareness of brain tumor segmentation and its classification, MRI scanning process and its operation, brain tumor classifications, and different segmentation methodologies

Алгоритм сегментации изображения с помощью искусственной нейронной сети без использования других изображений

oai:oai.radiooptics.elpub.ru:article/108The article suggests an algorithm of graphical image segmentation. The suggested algorithm uses a neural network to identify one particular pixel as belonged to the certain segment of an image. As a segmentation method, is used a method of growing areas based on comparing the nearest neighbors of one particular pixel. To make a decision about the similarity of two pixels, a three-layer perceptron is used. Three RGB color components are compared during processing. Thus, there are 6 neurons in the input layer of the neural network, namely 3 for the RGB component of the first pixel and 3 for the RGB component of the second one. In a specific implementation there are 50 neurons in the middle layer of the neural network. In the output layer of the neural network there are 2 neurons that represent similarity or difference of the comparing pixels. A training set of the neural network is formed using a specially generated impulse noise. There is a linear congruent generator of pseudo-random number used for noise generation. This generator is used to generate both the color and the coordinates of the noisy pixel. To form a training set, the certain number of noisy pixels is generated. In the article, this number is 10% of all pixels in the image. Then, for each damaged pixel, the training sets are formed so that all the nearest neighbors are considered to be in different clusters with a damaged pixel. A computer experiment was carried out both in automatic mode and in interactive one. The results of the experiment have shown that the algorithm provides fairly good training neural network for image segmentation without involving additional images.В работе представлен алгоритм выделения сегментов на графических изображениях. Предложенный алгоритм использует нейронную сеть для определения принадлежности отдельно взятого пикселя конкретному кластеру. В качестве метода сегментации используется метод выращивания областей, основанный на сравнивании ближайших соседей отдельно взятого пикселя изображения. Для принятия решения о схожести двух пикселей используется трёхслойный персептрон. Осуществляется сравнение трёх цветовых компонент пикселей в рамках модели RGB. Таким образом, во входном слое нейронной сети 6 нейронов – 3 для RGB компонент первого пикселя и 3 для RGB компонент второго. В данной реализации в среднем слое нейросети содержится 50 нейронов. В выходном слое содержится 2 нейрона, которые показывают схожесть или различие сравниваемых пикселей. Обучающее множество нейронной сети формируется с помощью специально сгенерированного импульсного шума. Для генерации шума используется линейный конгруэнтный генератор псевдослучайных чисел. Генератор используется для получения как цвета, так и координаты зашумлённого пикселя. Для формирования обучающего множества генерируется определённое количество зашумлённых пикселей. В рамках данной работы это количество равно 10% всех пикселей изображения. Затем для каждой испорченной точки происходит формирование обучающих наборов так, что все ближайшие соседи считаются находящимися в различных кластерах с повреждённым пикселем. Проведён компьютерный эксперимент как в автоматическом, так и в интерактивном режимах. Результаты эксперимента показали что алгоритм достаточно хорошо обучает нейронную сеть для сегментации изображения без привлечения дополнительных изображений

Land Cover Recognition using Min-Cut/Max-Flow Segmentation and Orthoimages

The geospatial information is significant for many socio-technical activities like urban planning, the prediction of natural hazards, the monitoring of land use, weather forecasting, cadastral surveys etc. It is possible to acquire geospatial information from a distance using remote sensing technologies, but remotely sensed images don’t have semantics without a previous recognition. The classification of geospatial information is expensive and time consuming process. The paper describes the automatic land cover recognition method, which is based on min-cut/max-flow segmentation. The raw data are othoimages with a high resolution. The proposed method is tested and evaluated by Cohen’s kappa coefficient

Machine Learning on Neutron and X-Ray Scattering

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom

Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

Deep Learning Approach for Chemistry and Processing History Prediction from Materials Microstructure

Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials’ microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition simulation of Fe–Cr–Co alloy created by the phase-field method. The mixed dataset, which includes both images, i.e., the morphology of Fe distribution, and continuous data, i.e., the Fe minimum and maximum concentration in the microstructures, are used as input data, and the spinodal temperature and initial chemical composition are utilized as the output data to train the proposed deep neural network. The proposed convolutional layers were compared with pretrained EfficientNet convolutional layers as transfer learning in microstructure feature extraction. The results show that the trained shallow network is effective for chemistry prediction. However, accurate prediction of processing temperature requires more complex feature extraction from the morphology of the microstructure. We benchmarked the model predictive accuracy for real alloy systems with a Fe–Cr–Co transmission electron microscopy micrograph. The predicted chemistry and heat treatment temperature were in good agreement with the ground truth