Region of Interest Identification on Low-Resolution Lateral Spine Radiography Image using Density-based and Ellipse-like Method
X-ray images of the lateral spine are important for diagnosing spine problems such as osteoporosis, bone fractures, and spondylosis. In order to identify bone diseases, often a series of images is required. These are taken using a low level of X-ray radiation to reduce the risk of exposure to overshoot radiation. Dual Energy X-ray Absorptiometry is a standard medical tool used to diagnose bone diseases. In addition, the spine alignment of each individual person is different others. Therefore, developing an approach that can identify the spine area is challenging. In this work, the algorithm for automatic identification of spine and vertebral bodies is proposed. The proposed method consists of three main steps. The first step, Bi-Histogram Equalization with adaptive sigmoid functions (BEASF), is a technique used for enhancing the spinal and vertebral bodies. In the second step, Density-based and Ellipse-like techniques are combined to locate the curve of the spine. For the third step, object improvement techniques are applied to predict the location of vertebral bodies. The experimental results show that the approach reached 79.67% of Area Overlap Ratio. 81.67% of the Precision value.
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Region of Interest Identification on Low-Resolution Lateral Spine Radiography Image using Density-based and Ellipse-like Method
Semantic Scholar · Computer Science · 2019
Abstract
X-ray images of the lateral spine are important for diagnosing spine problems such as osteoporosis, bone fractures, and spondylosis. In order to identify bone diseases, often a series of images is required. These are taken using a low level of X-ray radiation to reduce the risk of exposure to overshoot radiation. Dual Energy X-ray Absorptiometry is a standard medical tool used to diagnose bone diseases. In addition, the spine alignment of each individual person is different others. Therefore, developing an approach that can identify the spine area is challenging. In this work, the algorithm for automatic identification of spine and vertebral bodies is proposed. The proposed method consists of three main steps. The first step, Bi-Histogram Equalization with adaptive sigmoid functions (BEASF), is a technique used for enhancing the spinal and vertebral bodies. In the second step, Density-based and Ellipse-like techniques are combined to locate the curve of the spine. For the third step, object improvement techniques are applied to predict the location of vertebral bodies. The experimental results show that the approach reached 79.67% of Area Overlap Ratio. 81.67% of the Precision value.