3D Image Analysis and Artificial Intelligence for Bone Disease Classification

Akgündoğdu, Abdurrahim; Jennane, Rachid; Aufort, Gabriel; Benhamou, Claude Laurent; Uçan, Osman N.

doi:10.1007/s10916-009-9296-3

Cited by 22 publications

(15 citation statements)

References 37 publications

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“…In 2012 Thai, Hai, and Thuy [6] used the combination between feedforward artificial neural networks(FANN) with support vector machine (SVM) to classify Roman numeral images, the average classification rate was 86%. Akgundogdu et al [7], used support vector machine and genetic algorithm (GA) for bone sample images, with 12 training and 6 testing data, the success rate for SVM was equal 83.33%. Banerjee, et al [8], used artificial bee colony to classify band satellite images for different features, the accuracy rate for barren was equal 93.65%.…”

Section: Previous Workmentioning

confidence: 99%

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Image Clustering based on Artificial Intelligence Techniques

Khaleel¹

2014

AL-Rafidain Journal of Computer Sciences and Mathematics

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Clustering has been widely used in data analysis and pattern recognition and classification. The objective of cluster analysis is the classification of objects according to similarities among them, and organizing of data into groups. There are several algorithms for clustering large data sets or streaming data sets, Their aims are to organize a collection of data items into clusters. These such items are more similar to each other within a cluster, and different than they are in the other clusters. We have take the advantage of classification abilities of Artificial Intelligence Techniques (AITs) to classify images data set into a number of clusters. The Gath-Geva (GG) fuzzy clustering algorithm, Artificial Bee Colony algorithm(ABC), Radial Basis Function Network(RBF), and then combined Gath-Geva algorithm with (RBF) algorithm to produce Fuzzy RBF (FRBF) method were applied using images data set to classify this data set into a number of clusters (classes). Each cluster will contain data set with most similarity in the same cluster and most dissimilarity with the different clusters. We compute the classification rate, and false rate on this data set. Finally we make comparisons between results obtained after applying these algorithms on this images data set. The FRBF is better than the other three methods that applied in this research such as G-G, ABC, RBF, because the FRBF was obtained higher classification rate in testing state equal (96.8571) and low false alarm equal(3.1429).

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Section: Previous Workmentioning

confidence: 99%

“…Step3: compute the parameters of the model using equations(4), (6), (7). Step4: calculate the norm of distance utilizing equation (5).…”

Section: Gath-geva Fuzzy Clustering Algorithmmentioning

confidence: 99%

Image Clustering based on Artificial Intelligence Techniques

Khaleel¹

2014

AL-Rafidain Journal of Computer Sciences and Mathematics

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“…The specific descriptions of SVM and kNN are presented below. Brief Introduction of SVM Support vector machine is a supervised machinelearning method and has good performance on data classification (Akgundogdu et al, 2010). For a training dataset with two classes, SVM tries to find the best hyperplane to separate each class.…”

Section: Topological Featuresmentioning

confidence: 99%

Full supervised learning for osteoporosis diagnosis using micro‐CT images

Chen

et al. 2013

Microscopy Res & Technique

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Early osteoporosis diagnosis is of important significance for reducing fracture risk. Image analysis provides a new perspective for noninvasive diagnosis in recent years. In this article, we propose a novel method based on machine-learning method performed on micro-CT images to diagnose osteoporosis. The aim of this work is to find a way to more effectively and accurately diagnose osteoporosis on which many methods have been proposed and practiced. In this method, in contrast to the previously proposed methods in which features are analyzed individually, several features are combined to build a classifier for distinguishing osteoporosis group and normal group. Twelve features consisting of two groups are involved in our research, including bone volume/total volume (BV/TV), bone surface/bone volume (BS/BV), trabecular number (Tb.N), obtained from the software of micro-CT, and other four features from volumetric topological analysis (VTA). Support vector machine (SVM) method and k-nearest neighbor (kNN) method are introduced to create classifiers with these features due to their excellent performances on classification. In the experiment, 200 micro-CT images are used in which half are from osteoporosis patients and the rest are from normal people. The performance of the obtained classifiers is evaluated by precision, recall, and F-measure. The best performance with precision of 100%, recall of 100%, and F-measure of 100% is acquired when all the features are included. The satisfying result demonstrates that SVM and kNN are effective for diagnosing osteoporosis with micro-CT images.

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“…Few studies in the literature highlighted the relationship between architecture of the trabecular bone and osteoporosis diagnosis based on information extracted from CT and magnetic resonance (MR) images of a femur region. [5][6][7][8][9][10] The trabecular bone structural parameters extracted from CT and MR images were compared against BMD values. 5,6 The morphological parameters extracted from CT images were classified into osteoporotic from osteoarthritis samples.…”

Section: Introductionmentioning

confidence: 99%

“…5,6 The morphological parameters extracted from CT images were classified into osteoporotic from osteoarthritis samples. 7 Texture features derived from clinical CT images have helped bring out the architectural information on trabecular bone. 8 High-dose radiological methods helped more on extracting structural information of femur specimen and assessed the bone strength for the diagnosis of osteoporosis.…”

Section: Introductionmentioning

confidence: 99%

A computer-aided system for automatic extraction of femur neck trabecular bone architecture using isotropic volume construction from clinical hip computed tomography images

Vivekanandhan

Subramaniam²,

Mariamichael

2016

Proc Inst Mech Eng H

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Hip fractures due to osteoporosis are increasing progressively across the globe. It is also difficult for those fractured patients to undergo dual-energy X-ray absorptiometry scans due to its complicated protocol and its associated cost. The utilisation of computed tomography for the fracture treatment has become common in the clinical practice. It would be helpful for orthopaedic clinicians, if they could get some additional information related to bone strength for better treatment planning. The aim of our study was to develop an automated system to segment the femoral neck region, extract the cortical and trabecular bone parameters, and assess the bone strength using an isotropic volume construction from clinical computed tomography images. The right hip computed tomography and right femur dual-energy X-ray absorptiometry measurements were taken from 50 south-Indian females aged 30-80 years. Each computed tomography image volume was re-constructed to form isotropic volumes. An automated system by incorporating active contour models was used to segment the neck region. A minimum distance boundary method was applied to isolate the cortical and trabecular bone components. The trabecular bone was enhanced and segmented using trabecular enrichment approach. The cortical and trabecular bone features were extracted and statistically compared with dual-energy X-ray absorptiometry measured femur neck bone mineral density. The extracted bone measures demonstrated a significant correlation with neck bone mineral density (r > 0.7, p < 0.001). The inclusion of cortical measures, along with the trabecular measures extracted after isotropic volume construction and trabecular enrichment approach procedures, resulted in better estimation of bone strength. The findings suggest that the proposed system using the clinical computed tomography images scanned with low dose could eventually be helpful in osteoporosis diagnosis and its treatment planning.

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3D Image Analysis and Artificial Intelligence for Bone Disease Classification

Cited by 22 publications

References 37 publications

Image Clustering based on Artificial Intelligence Techniques

Image Clustering based on Artificial Intelligence Techniques

Full supervised learning for osteoporosis diagnosis using micro‐CT images

A computer-aided system for automatic extraction of femur neck trabecular bone architecture using isotropic volume construction from clinical hip computed tomography images

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