An Effective and Reliable Computer Automated Technique for Bone Fracture Detection

Abstract

INTRODUCTION:In the year 1895 the X-ray images were discovered. Since then the medical imaging has got advanced tremendously. Anyhow the methods of interpretation have started progressing only by the evolution of Computer aided Diagnosis(CAD). OBJECTIVES: To develop a Computer Aided Diagnosis (CAD) system to detect the bone fracture which helps the radiologists (or) the Orthopaedics by interpreting the medical images in short duration. METHODS: In this paper, an effective automated bone fracture detection is proposed using enhanced Haar Wavelet Transform, Scale-Invariant Feature Transform (SIFT) and back propagation neural network. The former two techniques are used for feature extraction and the latter one is used for classification of fracture images. Simultaneously, the usage of enhanced Haar Wavelet Transforms and SIFT are phenomenally improves the quality of the X-ray image. Further in this work, k-means clustering based 'Bag of Words' methods are used to extract enhanced features extracted from SIFT. The classification phase of this proposed technique uses the classical back propagation neural network that contains 1024 neurons in 3-layers. RESULTS: The experimental validation of this proposed scheme performed using nearly 300 different bone fractures x-ray images confirmed a better classification rate of 93.4%. CONCLUSIONS: The experimental results of the proposed computer aided technique are proven to be better than the detection technique facilitated with the traditional SIFT technique.

Deep Scale-Variant Network for Femur Trochanteric Fracture Classification with HP Loss

“…For example, Aruse et al [ 2 ] designed a three-dimensional computer model which computed the four scaphoid axes to measure the direction and angle of the fracture and then calculated the correlation of different fracture angles to prove that the direction of the fracture inclination was less influential in scaphoid fractures. Basha et al [ 3 ] designed an efficient and automatic bone fracture detection system which combined the enhanced Haar wavelet transform with scale-invariant feature transform (SIFT) to extract the image features and then input them to a neural network for bone fracture classification; the final experimental results indicated that the designed model could gain better classification performance compared with the SIFT method. Yin et al [ 4 ] explored the Tang classification system which was based on the three-dimensional image analysis system to achieve the automatic classification of the femoral intertrochanteric fracture, and it demonstrated that the proposed Tang classification system could be more reliable than other ones in this task.…”

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“…Another research in the health sector that applies the haar DWT filter and ANN is carried out to detect fractures with the addition of a Scale-Invariant Feature Transform feature extraction followed by K-means clustering with an accuracy of 93.4% [11]. Research to detect minor chronic cerebral hemorrhage using ANN and DWT feature extraction and Principal Component Analysis resulted in an accuracy of 88.43% Another study that applied moment invariant and ANN was conducted to classify weed, resulting in an accuracy of 92.5% [12].…”

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