Ihsan Ullah Khan, PhD

Ihsan Ullah Khan, PhD

Robotics Engineering

Conferences

Guidewire Tip Tracking using U-Net with Shape and Motion Constraints

Published:

In recent years, research has been carried out using a micro-robot catheter instead of classic cardiac surgery performed using a catheter. To accurately control the micro-robot catheter, accurate and decisive tracking of the guidewire tip is required. In this paper, we propose a method based on the deep convolutional neural network (CNN) to track the guidewire tip. To extract a very small tip region from a large image in video sequences, we first segment small tip candidates using a segmentation CNN architecture, and then extract the best candidate using shape and motion constraints. The segmentation-based tracking strategy makes the tracking process robust and sturdy. The tracking of the guidewire tip in video sequences is performed fully-automated in real-time, i.e., 71 ms per image. For two-fold cross-validation, the proposed method achieves the average Dice score of 88.07% and IoU score of 85.07%.

Recommended citation: Ihsan Ullah et al. (2019). Guidewire Tip Tracking using U-Net with Shape and Motion Constraints; 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). 1(1). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8669088

Catheter Synthesis in X-Ray Fluoroscopy with Generative Adversarial Networks

Published:

Accurate localization of catheters or guidewires in fluoroscopy images is important to improve the stability of intervention procedures as well as the development of surgical navigation systems. Recently, deep learning methods have been proposed to improve performance, however these techniques require extensive pixel-wise annotations. Moreover, the human annotation effort is equally expensive. In this study, we mitigate this labeling effort using generative adversarial networks (cycleGAN) wherein we synthesize realistic catheters in flouroscopy from localized guidewires in camera images whose annotations are cheaper to acquire. Our approach is motivated by the fact that catheters are tubular structures with varying profiles, thus given a guidewire in a camera image, we can obtain the centerline that follows the profile of a catheter in an X-ray image and create plausible X-ray images composited with such a centerline. In order to generate an image similar to the actual X-ray image, we propose a loss term that includes perceptual loss alongside the standard cycle loss. Experimental results show that the proposed method has better performance than the conventional GAN and generates images with consistent quality. Further, we provide evidence to the development of methods that leverage such synthetic composite images in supervised settings.

Recommended citation: Ihsan Ullah et al. (2019). Catheter Synthesis in X-Ray Fluoroscopy with Generative Adversarial Networks; MICCAI PRIME. 1(1). https://link.springer.com/content/pdf/10.1007/978-3-030-32281-6_13.pdf?pdf=inline%20link

Moving Vehicle Detection and Information Extraction Based on Deep Neural Network

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In recent years, vehicle recognition has become an important application in intelligent traffic monitoring and management. Vehicle analysis is an essential component in many intelligent applications, such as automatic toll collection, driver assistance systems, self-guided vehicles, intelligent parking systems, and traffic statistics (vehicle count, speed, and flow). The main goal of our study is to extract the information from the moving vehicles like their make, model and type. We address the vehicle detection and recognition problems using Deep Neural Networks (DNNs) approach. Our proposed approach outperforms state-of-the-art method. We first detect the moving vehicle based on frame difference and then extract the frontal part of the vehicle based on symmetrical filter, the frontal part of the vehicle is fed into the deep architecture for recognition. The Top 1 accuracy of proposed VMMTR algorithm is 96.31%.Our method achieves promising results on image.

Recommended citation: Ihsan Ullah et al. (2017). Moving Vehicle Detection and Information Extraction Based on Deep Neural Network; 2017 Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV); Athens, (2017).. 1(1). https://ihsan149.github.io/files/c3.pdf

License Plate Detection Based on Rectangular Features and Multilevel Thresholding

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Rapid advancement of technology in artificial intelligence and computer science knowledge and then feel the need to search and secure automated systems are because of the appearance of intelligent systems based on image processing and spread this knowledge. One of these intelligent systems is license plate recognition (LPR) system. LPR plays an important role in intelligent transportation system; however, plate region extraction is the key step before the final recognition. In this paper, an effective license plate extraction algorithm is proposed based on geometrical features and multilevel thresholding to identify and segment the license plate from the image. Experimental results show that the technique achieved promising accuracy.

Recommended citation: Ihsan Ullah et al. (2016). License Plate Detection Based on Rectangular Features and Multilevel Thresholding; 2016 IPCV.. 1(1). https://ihsan149.github.io/files/c7.pdf

Moving Object Detection Based on Background Subtraction

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Moving object detection is a task to identify the physical motion of an object in a specific region or area. Over the last few years, moving object detection has received much attention due to its wide range of applications like video surveillance, human motion analysis, robot navigation, event detection, anomaly detection, video conferencing, traffic analysis and security. In this paper, a framework is proposed for the evaluation of object detection algorithms in surveillance applications using background subtraction and Mixture of Gaussian. Experimental results show that our technique achieved promising accuracy.

Recommended citation: Ihsan Ullah et al. (2016). Moving Object Detection Based on Background Subtraction; 2016 Conference of KIISE, South Korea.. 1(1). https://ihsan149.github.io/files/c6.pdf

An Effective Algorithm for Shadow Removal from Moving Vehicles Based on Morphology

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In automatic video monitoring, real-time detection and in particular shadow elimination are critical to the correct moving objects segmentation since they severely affect the surveillance process. In this paper, we put forward a rapid and flexible approach in which vehicles detection is based on the Gaussian Mixture Model and shadow elimination is based on morphology and edge detection. Experimental results show that the technique achieved promising accuracy.

Recommended citation: Ihsan Ullah et al. (2016). An Effective Algorithm for Shadow Removal from Moving Vehicles Based on Morphology; 2016 International Symposium on Information Technology Convergence.. 1(1). https://ihsan149.github.io/files/c5.pdf

An Approach of Locating Korean Vehicle License Plate Based on Mathematical Morphology and Geometrical Features

Published:

In a vehicle license plate identification system, plate region detection is the crucial step before the ultimate recognition. In most of the traffic-related applications such as searching of stolen vehicles, road traffic monitoring, airport gate monitoring, speed checking and parking access control. This paper is focused on license plate detection, license plate detection in this paper is based on mathematical morphology and considers features like license plate width, height, ratio, and angle. The advantage of the proposed system is that it works for all types of license plates which differ in size and shapes. The proposed system archived promising results.

Recommended citation: Ihsan Ullah et al. (2016). An Approach of Locating Korean Vehicle License Plate Based on Mathematical Morphology and Geometrical Features; 2016 International Conference on Computational Science and Computational Intelligence (CSCI).. 1(1). https://ieeexplore.ieee.org/document/7881455