Система оптичного розпізнавання зображень з машинним навчанням

В бакалаврській роботі розглядається принцип побудови системи оптичного розпізнавання зображень з машинним навчанням, робота якої базується на алгоритмах навчання із штучним інтелектом. Як практична сторона реалізований програмний продукт, який моделює його роботу. Розроблений підхід дозволяє отримати покращені результати розпізнавання за рахунок застосування алгоритму К-середніх із багатошаровим вилученням ознак. Програмний продукт був створений на мові С++ у середовищі Visual Studio 2019 та із застосуванням програмних засобів MATLAB.The bachelor's thesis considers the principle of building a system of optical image recognition with machine learning, the work of which is based on learning algorithms with artificial intelligence. As a practical side implemented software product that simulates its operation. The developed approach allows to obtain improved recognition results through the use of the K-means algorithm with multilayer feature extraction. The software product was created in C++ in Visual Studio 2019 and using MATLAB software

Nuclei/Cell Detection in Microscopic Skeletal Muscle Fiber Images and Histopathological Brain Tumor Images Using Sparse Optimizations

Nuclei/Cell detection is usually a prerequisite procedure in many computer-aided biomedical image analysis tasks. In this thesis we propose two automatic nuclei/cell detection frameworks. One is for nuclei detection in skeletal muscle fiber images and the other is for brain tumor histopathological images. For skeletal muscle fiber images, the major challenges include: i) shape and size variations of the nuclei, ii) overlapping nuclear clumps, and iii) a series of z-stack images with out-of-focus regions. We propose a novel automatic detection algorithm consisting of the following components: 1) The original z-stack images are first converted into one all-in-focus image. 2) A sufficient number of hypothetical ellipses are then generated for each nuclei contour. 3) Next, a set of representative training samples and discriminative features are selected by a two-stage sparse model. 4) A classifier is trained using the refined training data. 5) Final nuclei detection is obtained by mean-shift clustering based on inner distance. The proposed method was tested on a set of images containing over 1500 nuclei. The results outperform the current state-of-the-art approaches. For brain tumor histopathological images, the major challenges are to handle significant variations in cell appearance and to split touching cells. The proposed novel automatic cell detection consists of: 1) Sparse reconstruction for splitting touching cells. 2) Adaptive dictionary learning for handling cell appearance variations. The proposed method was extensively tested on a data set with over 2000 cells. The result outperforms other state-of-the-art algorithms with F1 score = 0.96

Automated image classification via unsupervised feature learning by K-means

Indiana University-Purdue University Indianapolis (IUPUI)Research on image classification has grown rapidly in the field of machine learning. Many methods have already been implemented for image classification. Among all these methods, best results have been reported by neural network-based techniques. One of the most important steps in automated image classification is feature extraction. Feature extraction includes two parts: feature construction and feature selection. Many methods for feature extraction exist, but the best ones are related to deep-learning approaches such as network-in-network or deep convolutional network algorithms. Deep learning tries to focus on the level of abstraction and find higher levels of abstraction from the previous level by having multiple layers of hidden layers. The two main problems with using deep-learning approaches are the speed and the number of parameters that should be configured. Small changes or poor selection of parameters can alter the results completely or even make them worse. Tuning these parameters is usually impossible for normal users who do not have super computers because one should run the algorithm and try to tune the parameters according to the results obtained. Thus, this process can be very time consuming. This thesis attempts to address the speed and configuration issues found with traditional deep-network approaches. Some of the traditional methods of unsupervised learning are used to build an automated image-classification approach that takes less time both to configure and to run

Stacked Predictive Sparse Coding for Classification of Distinct Regions in Tumor Histopathology

Image-based classification of histology sections, in terms of distinct components (e.g., tumor, stroma, normal), pro-vides a series of indices for tumor composition. Further-more, aggregation of these indices, from each whole slide image (WSI) in a large cohort, can provide predictive mod-els of the clinical outcome. However, performance of the existing techniques is hindered as a result of large technical variations and biological heterogeneities that are always present in a large cohort. We propose a system that au-tomatically learns a series of basis functions for represent-ing the underlying spatial distribution using stacked pre-dictive sparse decomposition (PSD). The learned represen-tation is then fed into the spatial pyramid matching frame-work (SPM) with a linear SVM classifier. The system has been evaluated for classification of (a) distinct histological components for two cohorts of tumor types, and (b) colony organization of normal and malignant cell lines in 3D cell culture models. Throughput has been increased through the utility of graphical processing unit (GPU), and evalu-ation indicates a superior performance results, compared with previous research. 1