Affiliations: [a]
Department of Basic Sciences and Islamiat, University of Engineering and Technology, Peshawar, Pakistan
| [b]
Faculty of Engineering and Natural Science Bahcesehir University, Istanbul, Turkey
| [c]
Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Pakistan
| [d]
Department of Mathematics, Islamia College Peshawar, Pakistan
Correspondence:
[*]
Corresponding author. Noor Badshah, Department of Basic Sciences and Islamiat, University of Engineering and Technology, Peshawar, Pakistan. E-mail: noorbadshah@uetpeshawar.edu.pk.
Abstract: Joint segmentation and registration of images is a focused area of research nowadays. Jointly segmenting and registering noisy images and images having weak boundaries/intensity inhomogeneity is a challenging task. In medical image processing, joint segmentation and registration are essential methods that aid in distinguishing structures and aligning images for precise diagnosis and therapy. However, these methods encounter challenges, such as computational complexity and sensitivity to variations in image quality, which may reduce their effectiveness in real-world applications. Another major issue is still attaining effective joint segmentation and registration in the presence of artifacts or anatomical deformations. In this paper, a new nonparametric joint model is proposed for the segmentation and registration of multi-modality images having weak boundaries/noise. For segmentation purposes, the model will be utilizing local binary fitting data term and for registration, it is utilizing conditional mutual information. For regularization of the model, we are using linear curvature. The new proposed model is more efficient to segmenting and registering multi-modality images having intensity inhomogeneity, noise and/or weak boundaries. The proposed model is also tested on the images obtained from the freely available CHOAS dataset and compare the results of the proposed model with the other existing models using statistical measures such as the Jaccard similarity index, relative reduction, Dice similarity coefficient and Hausdorff distance. It can be seen that the proposed model outperforms the other existing models in terms of quantitatively and qualitatively.
Keywords: Image segmentation, , , , , image registration, linear curvature (LC), conditional mutual information (CMI), Jaccard similarity index (JSI)
