Analysis on Leg Bone Fracture Detection and Classification Using X-ray Images
Wint Wah Myint,
Khin Sandar Tun,
Hla Myo Tun
Issue:
Volume 3, Issue 3, September 2018
Pages:
49-59
Received:
19 August 2018
Accepted:
6 September 2018
Published:
10 October 2018
Abstract: Nowadays, computer aided diagnosis (CAD) system become popular because it improves the interpretation of the medical images compared to the early diagnosis of the various diseases for the doctors and the medical expert specialists. Similarly, bone fracture is a common problem due to pressure, accident and osteoporosis. Moreover, bone is rigid portion and supports the whole body. Therefore, the bone fracture is taken account of the important problem in recent year. Bone fracture detection using computer vision is getting more and more important in CAD system because it can help to reduce workload of the doctor by screening out the easy case. In this paper, lower leg bone (Tibia) fracture types recognition is developed using various image processing techniques. The purpose of this work is to detect fracture or non-fracture and classify type of fracture of the lower leg bone (tibia) in x-ray image. The tibia bone fracture detection system is developed with three main steps. They are preprocessing, feature extraction and classification to classify types of fracture and locate fracture locations. In preprocessing, Unshrap Masking (USM), which is the sharpening technique, is applied to enhance the image and highlight the edges in the image. The sharpened image is then processed by Harris corner detection algorithm to extract corner feature points for feature extraction. And then, two classification approaches are chosen to detect fracture or non-fracture and classify fracture types. For fracture or not classification, simple Decision Tree (DT) is employed and K-Nearest Neighbour (KNN) is used for classifying fracture types. In this work, Normal, Transverse, Oblique and Comminute are defined as the four fracture types. Moreover, fracture locations are pointed out by the produced Harris corner points. Finally, the outputs of the system are evaluated by two performance assessment methods. The first one is performance evaluation for fracture or non-fracture (normal) conditions using four possible outcomes such as TP, TN, FP and FN. The second one is to analysis for accuracy of each fracture type within error conditions using the Kappa assessment method. The programming software used to implement the system is MATLAB with wide range of image processing tools environment. The system produces 82% accuracy for classification fracture types.
Abstract: Nowadays, computer aided diagnosis (CAD) system become popular because it improves the interpretation of the medical images compared to the early diagnosis of the various diseases for the doctors and the medical expert specialists. Similarly, bone fracture is a common problem due to pressure, accident and osteoporosis. Moreover, bone is rigid porti...
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Development of Robot Navigation System with Collision Free Path Planning Algorithm
Issue:
Volume 3, Issue 3, September 2018
Pages:
60-68
Received:
3 August 2018
Accepted:
6 September 2018
Published:
12 October 2018
Abstract: Mobile robots have been successfully used in many fields due to their abilities to perform difficult tasks in hazardous environments, such as robot rescuing, space exploring and their various promising applications in the daily lives. Robot path planning is a key issue in robot navigation which is a kernel part in mobile robot technology. Robot path planning is to generate a collision-free path in an environment while satisfying some optimization criteria. Mobile robot path planning is a nondeterministic polynomial time (NP) problem, traditional optimization methods are not very effective to it, which are easy to plunge into local minimum. In this research work, an evolutionary algorithm to solve the robot path planning problem is devised. A method of robot path planning in partially unknown environments based on A star (A*) algorithm was proposed. The proposed algorithm allows a mobile robot to navigate through static obstacles and finds its path in order to reach from its initial position to the target without collision. In addition, the environment is partially unknown for the robot due to the limit detection range of its sensors. The robot processor updates its information during the motion. The simulations are performed in different static environments, and the results show that the robot reaches its target with colliding free obstacles. The optimal path is generated with this method when the robot reaches its target. The simulation results are developed by MATLAB environments.
Abstract: Mobile robots have been successfully used in many fields due to their abilities to perform difficult tasks in hazardous environments, such as robot rescuing, space exploring and their various promising applications in the daily lives. Robot path planning is a key issue in robot navigation which is a kernel part in mobile robot technology. Robot pat...
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