Abstract
Spina bifida is a birth defect caused by incomplete closing around the spinal cord. Spina bifida is diagnosed in a number of different ways. One approach involves searching for a deformity in the spinal axis via ultrasound. Although easy to apply, this approach requires a highly trained clinician to locate the abnormality due to the noise and distortion present in prenatal ultrasound images. Accordingly, visual examination of ultrasound images may be error prone and subjective. A computerized support system that would automatically detect the location of the spinal deformity may be helpful to the clinician in the diagnostic process. Such a software system first and foremost would require an algorithm for the identification of the entire (healthy or unhealthy) spine in the ultrasound image. This paper introduces a novel flocking dynamics based approach for reducing the size of the search space in the spine identification problem. Proposed approach accepts bone-like blobs on the ultrasound images as bird flocks and combine them into bone groups by calculating the migration path of each flock. Presented results reveal that the method is able to locate correct bones to be grouped together and reduce search space (i.e. number of bones) up to 68%.
Highlights
Machine learning based methods are frequently utilized in the literature for coping with automated classification problems on ultrasound images
The search space reduction algorithm along with the genetic algorithm (GA) is run for the fourteen fetal ultrasound images
We have proposed a novel nature inspired methodology for reducing the search space in the spine identification problem associated with the ultrasound images of SB cases
Summary
Machine learning based methods are frequently utilized in the literature for coping with automated classification problems on ultrasound images. In the first stage of the methodology, the search space is reduced in size by combining certain adjacent blobs that are likely to belong to the spine, into a single item. This is accomplished by accepting automatically each extracted bone blob as a bird flock and calculating its “migration path” to other flocks for clustering. The large size of the search space is a problem characteristics that has a direct effect on the classification ability of a meta-heuristic machine learning method[13]. By combining blobs into single items, we effectively decrease the total number of blobs and total number of their possible combinations, reducing the size of our search space[14]
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