In spite of the advantages of ease of imaging and low acquisition cost, ultrasound images are noisier and have poorer image quality than other imaging modalities like CT or MRI and hence require an experienced clinician for interpretation. Processing by automated segmentation assists clinicians and improves the accuracy of assessment by minimizing its subjective nature. Various methods for the segmentation of ultrasound images exist, but there is not much literature on the processing of 2D ultrasound images of the foot. This work aimed at developing a novel method for image processing in order to achieve automated segmentation of ultrasound images of the plantar soft tissue. Preprocessing of the ultrasound images was performed using the anisotropic diffusion filter followed by contrast enhancement. The Chan-Vese active contour method was used for segmentation. Our method took into account the difficulty of visualization of the tissues and bony structures in the foot and used an additional curvature parameter for segmentation. Assessing the changes in the biomechanical properties of the plantar soft tissue can be a potential application of this method especially in case of the diabetic foot.

Scoliosis has a detrimental effect on lung function. Since spine radiographs are commonly used for monitoring the progress of the disorder, semi-automated lung field segmentation from scoliosis radiographs is a primary step in further automation and processing. Existing lung field segmentation algorithms have been developed specifically for chest radiographs, which differ from spine radiographs in imaging aspects and appearance. The present work uses intensity profile processing followed by the optimization of a flexible polynomial template developed without prior training data. Right and left lungs are processed separately due to the presence of the cardiac shadow and differences in lung shape. Intensity profile processing is based on identification of the maxima and minima in segments of the horizontal profiles or their derivatives. The polynomial template represents an implicit shape model defined separately for each lung using three initial points chosen by the user. Thus, a unique template is generated for each case. The algorithm was tested on six sets of patient radiographs varying in severity of scoliosis and image quality. The proposed method successfully detects overlapping regions of the lung and the spine and has high accuracy even for severely deformed lungs.