Anterior osteoporosis classification in cervical vertebrae using fuzzy decision tree

Mustapha Aouache, Aini Hussain, Mohd Asyraf Zulkifley, Diyana Wan Mimi Wan Zaki, Hafizah Husain, Hamzaini Abdul Hamid

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Anterior Osteoporosis (AOs) in the cervical vertebrae is an osteoporosis complication and a common condition of vertebral irregularity caused by a decrease in bone density and strength, which can lead to fragile bone and fractures. Consequently, it is crucial to detect the AOs irregularity early so that appropriate pharmacological intervention can be done to reduce further complications. To do so via a computer approach, an efficient method that can provide high classification rate is required. Basing on the fuzzy logic theory, this article affords a new method for AOs (classes and severity) classification of the cervical radiography by designing a fuzzy decision tree (FDT) model. The method involves two main processed, namely i) segmentation process which employs the active shape model (ASM) based on the 9-anatomical points representation (9-APR) to segment the cervical vertebra shape boundary (C-VSB) and ii) fuzzy based feature extraction and classifier development, known as FDT method. The fuzzy set along with its membership functions are derived from the resulting C-VSB segment. It operates by extracting a specific angle descriptor (horizontal, vertical, and corner) as crisp input to the fuzzification inter-face to produce reasonable key indexing to the fuzzy interface system. Then, the defuzzification interface converts it into a crisp output that adequately represents the degree of AOs class and severity as appraisal values. The resulting fuzzy then acts as input to a basic concept of if-then rules called FDT to recognise and distinguish between vertebrae presented with/without AOs. Receiver operating characteristic (ROC) and area under curve (AUC) index evaluation methods are examined to offer quantitative evaluation between the medical ground truth versus FDT classifier predicted results. Results obtained on a set of 400 cervical vertebrae images indicate superb classification rate (R > 90 %) which suggest that the proposed FDT as an appropriate solution to AOs classification process for reliable vertebral fracture diagnosis. In summary, the findings confirmed the effectiveness of FDT as an excellent classifier to recognize and differentiate AOs classes and severity thus, able to provide important basis for pathology.

Original languageEnglish
Pages (from-to)1-35
Number of pages35
JournalMultimedia Tools and Applications
DOIs
Publication statusAccepted/In press - 17 Feb 2017

Fingerprint

Decision trees
Classifiers
Bone
Radiography
Fuzzy rules
Pathology
Membership functions
Fuzzy sets
Fuzzy logic
Feature extraction

Keywords

  • 9-anatomical points
  • Anterior osteoporosis
  • ASM model
  • Cervical radiography
  • Classification approach
  • Fuzzy decision tree
  • ROC curve

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

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title = "Anterior osteoporosis classification in cervical vertebrae using fuzzy decision tree",
abstract = "Anterior Osteoporosis (AOs) in the cervical vertebrae is an osteoporosis complication and a common condition of vertebral irregularity caused by a decrease in bone density and strength, which can lead to fragile bone and fractures. Consequently, it is crucial to detect the AOs irregularity early so that appropriate pharmacological intervention can be done to reduce further complications. To do so via a computer approach, an efficient method that can provide high classification rate is required. Basing on the fuzzy logic theory, this article affords a new method for AOs (classes and severity) classification of the cervical radiography by designing a fuzzy decision tree (FDT) model. The method involves two main processed, namely i) segmentation process which employs the active shape model (ASM) based on the 9-anatomical points representation (9-APR) to segment the cervical vertebra shape boundary (C-VSB) and ii) fuzzy based feature extraction and classifier development, known as FDT method. The fuzzy set along with its membership functions are derived from the resulting C-VSB segment. It operates by extracting a specific angle descriptor (horizontal, vertical, and corner) as crisp input to the fuzzification inter-face to produce reasonable key indexing to the fuzzy interface system. Then, the defuzzification interface converts it into a crisp output that adequately represents the degree of AOs class and severity as appraisal values. The resulting fuzzy then acts as input to a basic concept of if-then rules called FDT to recognise and distinguish between vertebrae presented with/without AOs. Receiver operating characteristic (ROC) and area under curve (AUC) index evaluation methods are examined to offer quantitative evaluation between the medical ground truth versus FDT classifier predicted results. Results obtained on a set of 400 cervical vertebrae images indicate superb classification rate (R > 90 {\%}) which suggest that the proposed FDT as an appropriate solution to AOs classification process for reliable vertebral fracture diagnosis. In summary, the findings confirmed the effectiveness of FDT as an excellent classifier to recognize and differentiate AOs classes and severity thus, able to provide important basis for pathology.",
keywords = "9-anatomical points, Anterior osteoporosis, ASM model, Cervical radiography, Classification approach, Fuzzy decision tree, ROC curve",
author = "Mustapha Aouache and Aini Hussain and Zulkifley, {Mohd Asyraf} and {Wan Zaki}, {Diyana Wan Mimi} and Hafizah Husain and {Abdul Hamid}, Hamzaini",
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doi = "10.1007/s11042-017-4468-5",
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T1 - Anterior osteoporosis classification in cervical vertebrae using fuzzy decision tree

AU - Aouache, Mustapha

AU - Hussain, Aini

AU - Zulkifley, Mohd Asyraf

AU - Wan Zaki, Diyana Wan Mimi

AU - Husain, Hafizah

AU - Abdul Hamid, Hamzaini

PY - 2017/2/17

Y1 - 2017/2/17

N2 - Anterior Osteoporosis (AOs) in the cervical vertebrae is an osteoporosis complication and a common condition of vertebral irregularity caused by a decrease in bone density and strength, which can lead to fragile bone and fractures. Consequently, it is crucial to detect the AOs irregularity early so that appropriate pharmacological intervention can be done to reduce further complications. To do so via a computer approach, an efficient method that can provide high classification rate is required. Basing on the fuzzy logic theory, this article affords a new method for AOs (classes and severity) classification of the cervical radiography by designing a fuzzy decision tree (FDT) model. The method involves two main processed, namely i) segmentation process which employs the active shape model (ASM) based on the 9-anatomical points representation (9-APR) to segment the cervical vertebra shape boundary (C-VSB) and ii) fuzzy based feature extraction and classifier development, known as FDT method. The fuzzy set along with its membership functions are derived from the resulting C-VSB segment. It operates by extracting a specific angle descriptor (horizontal, vertical, and corner) as crisp input to the fuzzification inter-face to produce reasonable key indexing to the fuzzy interface system. Then, the defuzzification interface converts it into a crisp output that adequately represents the degree of AOs class and severity as appraisal values. The resulting fuzzy then acts as input to a basic concept of if-then rules called FDT to recognise and distinguish between vertebrae presented with/without AOs. Receiver operating characteristic (ROC) and area under curve (AUC) index evaluation methods are examined to offer quantitative evaluation between the medical ground truth versus FDT classifier predicted results. Results obtained on a set of 400 cervical vertebrae images indicate superb classification rate (R > 90 %) which suggest that the proposed FDT as an appropriate solution to AOs classification process for reliable vertebral fracture diagnosis. In summary, the findings confirmed the effectiveness of FDT as an excellent classifier to recognize and differentiate AOs classes and severity thus, able to provide important basis for pathology.

AB - Anterior Osteoporosis (AOs) in the cervical vertebrae is an osteoporosis complication and a common condition of vertebral irregularity caused by a decrease in bone density and strength, which can lead to fragile bone and fractures. Consequently, it is crucial to detect the AOs irregularity early so that appropriate pharmacological intervention can be done to reduce further complications. To do so via a computer approach, an efficient method that can provide high classification rate is required. Basing on the fuzzy logic theory, this article affords a new method for AOs (classes and severity) classification of the cervical radiography by designing a fuzzy decision tree (FDT) model. The method involves two main processed, namely i) segmentation process which employs the active shape model (ASM) based on the 9-anatomical points representation (9-APR) to segment the cervical vertebra shape boundary (C-VSB) and ii) fuzzy based feature extraction and classifier development, known as FDT method. The fuzzy set along with its membership functions are derived from the resulting C-VSB segment. It operates by extracting a specific angle descriptor (horizontal, vertical, and corner) as crisp input to the fuzzification inter-face to produce reasonable key indexing to the fuzzy interface system. Then, the defuzzification interface converts it into a crisp output that adequately represents the degree of AOs class and severity as appraisal values. The resulting fuzzy then acts as input to a basic concept of if-then rules called FDT to recognise and distinguish between vertebrae presented with/without AOs. Receiver operating characteristic (ROC) and area under curve (AUC) index evaluation methods are examined to offer quantitative evaluation between the medical ground truth versus FDT classifier predicted results. Results obtained on a set of 400 cervical vertebrae images indicate superb classification rate (R > 90 %) which suggest that the proposed FDT as an appropriate solution to AOs classification process for reliable vertebral fracture diagnosis. In summary, the findings confirmed the effectiveness of FDT as an excellent classifier to recognize and differentiate AOs classes and severity thus, able to provide important basis for pathology.

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