Computer vision in healthcare applications

  • Medical imaging has attracted increasing attention in recent years due to its vital component in healthcare applications.
  • Computer vision technique has shown great application in surgery and therapy of some diseases.

The research of computer vision, imaging processing and pattern recognition has made substantial progress during the past several decades. Also, medical imaging has attracted increasing attention in recent years due to its vital component in healthcare applications. Investigators have published a wealth of basic science and data documenting the progress and healthcare application on medical imaging.

Analysis of medical image

This theme attempts to address the improvement and new techniques on the analysis methods of medical image. First, integration of multimodal information carried out from different diagnostic imaging techniques is essential for a comprehensive characterisation of the region under examination. Therefore, image coregistration has become crucial both for qualitative visual assessment and for quantitative multiparametric analysis in research applications. S. Monti et al. in Italy “An Evaluation of the Benefits of Simultaneous Acquisition on PET/MR Coregistration in Head/Neck Imaging” compare and assess the performance between the traditional coregistration methods applied to PET and MR acquired as single modalities and the obtained results with the implicitly coregistration of a hybrid PET/MR, in complex anatomical regions such as the head/neck (HN). The experimental results show that hybrid PET/MR provides a higher registration accuracy than the retrospectively coregistered images.

Also read: What is machine learning and computer vision?

Also read: 5 applications of computer vision

Computer vision for predictive analytics and therapy

Computer vision technique has shown great application in surgery and therapy of some diseases. Recently, three-dimensional (3D) modeling and rapid prototyping technologies have driven the development of medical imaging modalities, such as CT and MRI. P. Gargiulo et al. in Iceland “New Directions in 3D Medical Modeling: 3D-Printing Anatomy and Functions in Neurosurgical Planning” combine CT and MRI images with DTI tractography and use image segmentation protocols to 3D model the skull base, tumor, and five eloquent fiber tracts. The authors provide a great potential therapy approach for advanced neurosurgical preparation.

The elderly is easy to fall and it will harm the body and accordingly has serious negative mental impacts on them. T.-H. Lin et al. in Taiwan “Fall Prevention Shoes Using Camera-Based Line-Laser Obstacle Detection System” design an interesting line-laser obstacle detection system to prevent the elderly from falls. In the system, a laser line passes through a horizontal plane and has a specific height to the ground, and optical axis in a camera has a specific inclined angle to the plane, and hence, the camera can observe the laser pattern to obtain the potential obstacles. Unfortunately, this system designed is useful mainly for indoor applications instead of outdoor environment.

Fundamental algorithms for medical images

Organ segmentation is a prerequisite for CAD systems. In fact, the segmentation algorithm is the most important and basic for image processing and also enhance the level of disease prediction and therapy. C. Pan et al. in China “Leukocyte Image Segmentation Using Novel Saliency Detection Based on Positive Feedback of Visual Perception” use the ensemble of polyharmonic extreme learning machine (EPELM) and positive feedback of perception to detect salient objects, which is totally data-driven without any prior knowledge and labeled samples compared with the existed algorithms. A positive feedback module based on EPELM focuses on fixation area for the purpose of intensifying objects, inhibiting noises, and promoting saturation in perception. Experiments on several standard image databases show that the novel algorithm outperforms the conventional saliency detection algorithms and also segments nucleated cells successfully in different imaging conditions.

Their researches identify the critical need for clinical and theory prospective of medical images. This blog brings about various new developments in computer vision about medical images and clinical application.


Revel Cheng

Revel Cheng is an intern news reporter at Blue Tech Wave specialising in Fintech and Blockchain. She graduated from Nanning Normal University. Send tips to

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