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Novel multiple pooling and local phase quantization stable feature extraction techniques for automated classification of brain infarcts

Dogan Sengul, Barua Prabal Datta, Baygin Mehmet, Chakraborty Subrata, Ciaccio Edward J., Tuncer Turker, Abd Kadir Khairul Azmi, Md Shah Mohammad Nazri, Azman Raja Rizal, Lee Chin Chew, Ng Kwan Hoong, Acharya U. Rajendra

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 3

815--828

This study aims to introduce a hand-crafted machine learning method to classify ischemic and hemorrhagic strokes with satisfactory performance. In the first step of this work, a new CT brain for images dataset was collected for stroke patients. A highly accurate hand-crafted machine learning method is developed and tested for these cases. This model uses preprocessing, feature creation using a novel pooling method (it is named P9), a local phase quantization (LPQ) operator, and a Chi2-based selector responsible for selecting the most significant features. After that, classification is done using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation (CV). The novel aspect of this model is the P9 pooling method. The inspiration for this pooling method was drawn from the deep learning models, where features are extracted with multiple layers using a convolution operator applied to the pooling method. However, pooling decompositions have a routing problem. The P9 pooling function creates nine decomposed models, hence the name. The LPQ feature extractor is applied to images to generate sub-bands for feature generation. The Chi2 selector is then employed to select the most significant features from the created feature vector, and these features are utilized for the classification using the k-nearest neighbor algorithm (kNN). The introduced P9-LPQ feature extraction-based learning model attained over 98% classification accuracy in all cases. The results obtained in this paper show that the proposed method can successfully classify stroke types. For this reason, the developed model can pre-diagnose stroke types in the future.

Analysis of the effectiveness of selected segmentation methods of anatomical brain structures

Dzierżak R., Michalska M.

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

2018

T. 8, nr 2

58--61

An important aspect of analysis medical images is acknowledging the role of the segmentation process of individual anatomical structures. This process allows to show the most important diagnostic details. Owing to the segmentation the areas of interest (ROI) it is possible to adapt the methods of further image analysis considering the specification of selected elements. This process has been widely used in medical diagnostics. The article presents the use of segmentation by thresholding, segmentation by region growth and by edge detection to extract the parts of the human brain the user is interested in. The series of MRI (magnetic resonance imaging) images were used. The aim of the research was to develop the methods that would allow comparing the effectiveness various types of anatomical brain structures’ segmentation in two dimensions. The above methods present the different impact that selected types of segmentation, masks or parameters have on the most accurate depiction of a selected human brain element.

Istotnym aspektem analizy obrazów medycznych jest dostrzeżenie roli procesu segmentacji poszczególnych struktur anatomicznych. Proces ten pozwala na ukazanie najistotniejszych pod względem diagnostycznym szczegółów. Dzięki segmentacji obszarów zainteresowania (ROI) możliwe jest odpowiednie dostosowanie metod dalszej analizy obrazów uwzględniając specyfikę wybranych elementów. Proces ten znalazł rozległe zastosowanie w diagnostyce medycznej. W artykule przedstawiono wykorzystanie segmentacji przez progowanie, przez rozrost regionów oraz przez wykrywanie krawędzi, w celu wyodrębnienia interesujących użytkownika części ludzkiego mózgu. Wykorzystano serie obrazów MRI (rezonans magnetyczny). Celem badań było opracowanie metod, które pozwolą porównać skuteczność różnych typów segmentacji struktur anatomicznych mózgu w dwóch wymiarach. Zaprezentowane metody pokazują różny wpływ wybranych rodzajów segmentacji, masek czy parametrów na dokładniejsze ukazanie poszczególnych elementów ludzkiego mózgu.

Segmentation of brain MR images using rough set based intuitionistic fuzzy clustering

Dubey Y. K., Mushrif M. M., Mitra K.

Biocybernetics and Biomedical Engineering

2016

Vol. 36, no. 2

413--426

Intuitionistic fuzzy sets and rough sets are widely used for medical image segmentation, and recently combined together to deal with uncertainty and vagueness in medical images. In this paper, a rough set based intuitionistic fuzzy c-means (RIFCM) clustering algorithm is proposed for segmentation of the magnetic resonance (MR) brain images. Firstly, we proposed a new automated method to determine the initial values of cluster centroid using intuitionistic fuzzy roughness measure, obtained by considering intuitionistic fuzzy histon as upper approximation of rough set and fuzzy histogram as lower approximation of rough set. A new intuitionistic fuzzy complement function is proposed for intuitionistic fuzzy image representation to take into account intensity inhomogeneity and noise in brain MR images. The results of segmentation of proposed algorithm are compared with the existing rough set based fuzzy clustering algorithms, intuitionistic fuzzy clustering and bias corrected fuzzy clustering algorithm. Experimental results demonstrate the superiority of proposed algorithm.