Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated.

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Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System.

The goal of this paper is to present a critical review on the main systems that use artificial intelligence to identify groups at risk for osteoporosis or fractures. The systems considered for this study were those that fulfilled the following requirements: range of coverage in diagnosis, low cost and capability to identify more significant somatic factors. A bibliographic research was done in the databases, PubMed, IEEExplorer Latin American and Caribbean Center on Health Sciences Information (LILACS), Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, Web of Science, and Science Direct searching the terms “Neural Network”, “Osteoporosis Machine Learning” and “Osteoporosis Neural Network”. Studies with titles not directly related to the research topic and older data that reported repeated strategies were excluded. The search was carried out with the descriptors in German, Spanish, French, Italian, Mandarin, Portuguese and English; but only studies written in English were found to meet the established criteria. Articles covering the period 2000–2017 were selected; however, articles prior to this period with great relevance were included in this study. Based on the collected research, it was identified that there are several methods in the use of artificial intelligence to help the screening of risk groups of osteoporosis or fractures. However, such systems were limited to a specific ethnic group, gender or age. For future research, new challenges are presented. It is necessary to develop research with the unification of different databases and grouping of the various attributes and clinical factors, in order to reach a greater comprehensiveness in the identification of risk groups of osteoporosis. For this purpose, the use of any predictive tool should be performed in different populations with greater participation of male patients and inclusion of a larger age range for the ones involved. The biggest challenge is to deal with all the data complexity generated by this unification, developing evidence-based standards for the evaluation of the most significant risk factors.

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Artificial intelligence on the identification of risk groups for osteoporosis, a general review.

Objectives:This study proposed a new automated screening system based on a hybrid genetic swarm fuzzy (GSF) classifier using digital dental panoramic radiographs to diagnose females with a low bone mineral density (BMD) or osteoporosis.Methods:The geometrical attributes of both the mandibular cortical bone and trabecular bone were acquired using previously developed software. Designing an automated system for osteoporosis screening involved partitioning of the input attributes to generate an initial membership function (MF) and a rule set (RS), classification using a fuzzy inference system and optimization of the generated MF and RS using the genetic swarm algorithm. Fivefold cross-validation (5-FCV) was used to estimate the classification accuracy of the hybrid GSF classifier. The performance of the hybrid GSF classifier has been further compared with that of individual genetic algorithm and particle swarm optimization fuzzy classifiers.Results:Proposed hybrid GSF classifier in identifying low BMD or ost...

Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches.

Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale complexity. The fractal feature is a compact descriptor used to give a numerical measure of the degree of irregularity of the medical images. This descriptor property does not give ownership of the local image structure. In this paper, we present a combination of this parameter based on Box Counting with GLCM Features. This powerful combination has proved good results especially in classification of medical texture from MRI and CT Scan images of trabecular bone. This method has the potential to improve clinical diagnostics tests for osteoporosis pathologies.

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A Combined Method Of Fractal And GLCM Features For MRI And CT Scan Images Classification

Developmental molecular and functional cerebellar alterations induced by PCP4/PEP19 overexpression: implications for Down syndrome.

Osteoporosis is considered as a major public health threat. It is characterized by a decrease in the density of bone, decreasing its strength and leading to an increased risk of fracture. In this work, the morphological, topological and mechanical characteristics of 2 populations of arthritic and osteoporotic trabecular bone samples are evaluated using artificial intelligence and recently developed skeletonization algorithms. Results show that genetic algorithms associated with image processing tools can precisely separate the 2 populations.

Genetic algorithm and image processing for osteoporosis diagnosis

In this paper, a simple and practical two-dimensional finite element (FE) model coupled to a quasi-brittle damage law has been developed to describe the initiation and progressive propagation of damage of human proximal femur under quasi-static load until complete fracture. In order to validate the model, 10 human proximal femurs were tested till complete fracture under one-legged stance quasi-static load. During each load step, visual image measurements of full-field real-time strain was achieved using a digital image correlation technique consisting in an optical image system with recording cameras linked to a computer with image-processing software. Two-dimensional FE femur models were derived by the projection of micro-computed tomography scans and the specimen fractures were simulated using the same loads and boundary conditions as in the experimental tests. The predicted and optically measured strain field magnitudes and distributions were compared for the 10 specimens. Three femurs were use...

Using visual image measurements to validate a novel finite element model of crack propagation and fracture patterns of proximal femur

The form primitives of trabecular bone can be modelled as a complex structure of rods and plates. The classification of these primitives helps in simulating the physical or mechanical properties which are usually determined not only by the porosity of the object, but also by the arrangement of structure primitives in the 3D space. Based on the classified trabecular bone primitives, we propose in this work a finite element model that is both simple and fast while preserving the accuracy of the analysis. Rods are modelled as beams with circular cross sections. For the plate-like primitives, a triangulation method is used to characterise the behaviour of the corresponding shell elements. Using the proposed method, multiple features can be generated and combined in a statistical discriminant analysis in order to study the mechanical behaviour of selected trabecular bone samples. A clinical study was conducted on two populations of arthritic and osteoporotic bone samples. The results show the ability of our beam/shell model to discriminate the two populations.

Mechanical assessment of trabecular bone stiffness using hybrid skeleton and finite element analysis

In this paper, a semi automatic method is proposed for the segmentation of mice cerebral structures (brain, cerebellum and hippocampus) in MR images. First, a Chan-Vese method is applied on the axial images to segment the brain volume. The method takes into account the special shape of the brain mice. Second, variational atlases are constructed by manual segmentation of various MRI brain images of reference and Trisomy 21 mice. These atlases are then registered on true data to assist the Chan-Vese segmentation of the cerebellum and the hippocampus. This semi automatic method makes that human intervention is limited and the tedious manual handling is greatly reduced. Results have shown that the brain volumes estimated by the method are identical to expert manually estimated volumes. The new method was used in the analysis of the cerebral malformations linked to Trisomy 21: no significant difference of the cerebral structures between Trisomy 21 mice and the control ones was found.

Segmentation of mice cerebral structures: application in Trisomy 21

Image registration is an important step for a great variety of applications such as remote sensing, medical imaging, and multi-sensor fusion-based target recognition. The objective is to find, in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images for high quality products. In the broad area of global optimization methods, Genetic Algorithms form a widely accepted trade-off between global and local search strategies. They are well-investigated and have proven their applicability in many fields. In this paper, we present an efficient 2D point based rigid image registration method integrating the advantage of the robustness of GAs in finding the best transformation between two images. The algorithm is applied for registering SPOT images and the results show the effectiveness of this approach.

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Ahmad Almhdie-Imjabber

Papers

Genetic algorithm and image processing for osteoporosis diagnosis

Using visual image measurements to validate a novel finite element model of crack propagation and fracture patterns of proximal femur

Mechanical assessment of trabecular bone stiffness using hybrid skeleton and finite element analysis

Segmentation of mice cerebral structures: application in Trisomy 21

A 2d rigid point registration for satellite imaging using genetic algorithms