Key Research Themes

• Multidimensional Signal Processing
• Biomedical Imaging and Analysis
• Multimedia Compression and Communication
• Machine Learning

Research Leads

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Selected Publications

2024

• Shaukat, Furqan, Syed Muhammad Anwar, Abhijeet Parida, Van Khanh Lam, Marius George Linguraru, and Mubarak Shah. "Lung-cadex: Fully automatic zero-shot detection and classification of lung nodules in thoracic ct images." In International Workshop on Machine Learning in Medical Imaging, pp. 73-82. Cham: Springer Nature Switzerland, 2024.
• M. B. Khan, F. Shaukat, M. Abdullah, J. Mir and Gulistan Raja, "Fully Automatic Lung Segmentation in Thoracic CT Images using K-means Thresholding", 4th International Conference on Key Enabling Technologies, 1-2 Sep. 2024, Dublin, Ireland.
• Laiba Arshad and Junaid Mir, "Investigation of Audio Features for Heart Murmur Detection and Classification," in 2nd International Multidisciplinary Conference on Emerging Trends in Engineering Technology (IMCEET), Khairpur Mirs, Pakistan, Mar. 2024
• Majid Riaz, Muhammad Majid, and Junaid Mir, "High Dynamic Range Multimedia: Better Affective Agent for Human Emotional Experience," Multimedia Tools and Applications, vol. 83, pp. 25503–25518, 2024. (IF=3.00)
• Mati Ullah, Junaid Mir, Syed Sameed Husain, Muhammad Laiq Ur Rahman Shahid, and Afaq Ahmad, "Concrete Forensic Analysis using Deep Learning-based Coarse Aggregate Segmentation," Automation in Construction, vol. 162, pp. 105372, 2024. (IF=9.60)
• Rana Ehtisham, Waqas Qayyum, Charles Camp, Vagelis Plaveris, Junaid Mir, Qaiser Uz Zaman Khan, and Afaq Ahmad, "Computing the Characteristics of Defects in Wooden Structures using Image Processing and CNN," Automation in Construction, vol. 158, pp. 105211, 2024. (IF=9.60)
• Zulkaif Sajjad, Hassan Alam, Nouman Ahmed, Areeba Shahzad Raja, Junaid Mir, and Furqan Shaukat, "Deepfake Urdu Audio Detection using Spectral Features for Automatic Speaker Verification," in 26th IEEE International MultiTopic Conference (INMIC), Karachi, Pakistan, Dec. 2024.
• Muhammad Umair, Hanzala Nadeem, and Junaid Mir, "Density Control Smart Traffic Signals using Image Processing," in 3rd IEEE International Conference on Engineering & Computing Technologies (ICECT), Islamabad, Pakistan, May 2024, pp. 1-6. 
•  Y. Rizwan and Gulistan Raja, "A Comprehensive Review of Machine Learning Applications in State Assessment and Control of Power Electronic Converters",  4th International Conference on Key Enabling Technologies, 1-2 Sep. 2024, Dublin, Ireland.
• E. Khan and Gulistan Raja, “Advancing Crop Protection through Convolutional Neural Networks: A Multi-Plant Disease Classification Study”, IJANSER, Vol. 8, No. 3, June 2024, pp. 68-76.
• M. Masood and Gulistan Raja, "Aberrance Repressed Multi-feature Integrated Correlation Filter with Adaptive Learning for Visual Object Tracking", M.U. Research Journal of Engineering & Technology, Vol. 43, No. 4, Oct. 2024, pp. 14-28.

2023

• M. U. Abdullah and Gulistan Raja, "Computer-Aided Diagnosis Systems for Diabetic Retinopathy: A comprehensive Review", 3rd International Conference on Key Enabling Technologies, Istanbul, Turkey, 28-30 Aug. 2023. 
• Rana Ehtisham, Waqas Qayyum, Charles Camp, Vagelis Plaveris, Junaid Mir, Qaiser Uz Zaman Khan, and Afaq Ahmad, "Classification of Defects in Wooden Structures using Pre-Trained Models of Convolutional Neural Network," Case Studies in Construction Materials, vol. 19, pp. e02530, 2023. (IF=6.50)
• Waqas Qayyum, Rana Ehtisham, Alireza Bahrami, Junaid Mir, Qaiser Uz Zaman Khan, Afaq Ahmad, and Yasin Onuralp Ozkilic, "Predicting Characteristics of Cracks in Concrete Structure using Convolutional Neural Network and Image Processing," Frontiers in Materials, vol. 10, 2023. (IF=2.60)
• Waqas Qayyum, Rana Ehtisham, Alireza Bahrami, Charles Camp, Junaid Mir, and Afaq Ahmad, "Assessment of Convolutional Neural Network Pre-Trained Models for Detection and Orientation of Cracks," Materials, vol. 16, no. 2, pp. 826, 2023. (IF=3.10)
• Mahnoor Malik, Junaid Mir, and Afaq Ahmad, "Concrete Aggregate Segmentation for Structural Health Monitoring," in 8th Multi-Disciplinary Student Research International Conference (MDSRIC), Wah Cantt, Pakistan, Dec. 2023.
• M. Masood and Gulistan Raja, " Multi-feature Integration with Adaptive Learning based Correlation Filter for Visual Object Tracking", International Multidisciplinary Conference on Emerging Research & Technology (IMCERT), Karachi, Jan. 4-5, 2023.
• M. Usman and Gulistan Raja, " A Multi-Plant Disease Classification using Convolutional Neural Network", 3rd International Conference on Key Enabling Technologies, Istanbul, Turkey, 28-30 Aug. 2023. 

2022

• Rehan Khan and Junaid Mir, "White Blood Cells Segmentation and Classification using U-Net CNN and Hand-crafted Features," in IEEE International Conference on IT & Industrial Technologies (ICIT), Chiniot, Pakistan, Oct. 2022, pp. 1–7.
• Syed Sameed Husain, Junaid Mir, Syed Muhammad Anwar, Waqas Rafique, and Muhammad Obaid Ullah, "Development and validation of a deep learning-based algorithm for drowsiness detection in facial photographs," Multimedia Tools and Applications, vol. 81, pp. 20425–20441, 2022. (IF=3.60)
• Muhammad Imran Waris, Vagelis Pllevris, Junaid Mir, Nida Chairman, and Afaq Ahmad, "An alternative approach for measuring the mechanical properties of hybrid concrete through image processing and machine learning," Construction and Building Materials, vol. 328, pp. 126899, 2022. (IF=7.40)
• Waqas Qayyum, Rana Ehtisham, Charles V. Camp, Junaid Mir, and Afaq Ahmad, "Detecting cracks with Convolution Neural Network (CNN) with Variable image dataset," in 2nd International Conference on Recent Advances in Civil Engineering and Disaster Management (ICEEDM), Peshawar, Pakistan, Dec. 2022, pp. 166-170.
• Muhammad Shan Saleem, Gulistan Raja, and Junaid Mir, "Guided Image Filter Inspired Improved Single Image Dehazing Method," in 2nd International Conference on Engineering & Computing Technologies (ICECT), Islamabad, Pakistan, Nov. 2022. 
• Rana Ehtisham, Charles V. Camp, Junaid Mir, Nida Chairman, and Afaq Ahmad, "Evaluation of Pre-trained ResNet and MobileNetV2 CNN Models for the Concrete Crack Detection Crack Orientation Classification," in International Conference on Advances in Civil and Environmental Engineering, Taxila, Pakistan, Feb. 2022.
• M. Qasim and Gulistan Raja, “SPIDE-Net: Spectral Prior-based Image Dehazing and Enhancement Network”, IEEE Access, Vol.10, Nov. 2022, pp. 120296-120311.
• M. W. Malik, Q. Gull and Gulistan Raja, "Blockchain-based Security and Privacy-aware Protocol for Vehicular Ad Hoc Network", 19th International Bhurban Conference on Applied Sciences and Technologies (IBCAST 2022), Islamabad, 16-20 Aug. 2022.

2021

• M. Ayaz, F Shaukat, and Gulistan Raja, "Ensemble Learning Based Automatic Detection of Tuberculosis in Chest X-ray Images using Hybrid Feature Descriptors",  Physical and Engineering Sciences in Medicine, Vol. 44, Issue 1, Mar. 2021, pp.183-194.
• S. Shakeel and Gulistan Raja, "Classification of Breast Cancer from Mammogram images using Deep Convolution Neural Networks", 18th International Bhurban Conference on Applied Sciences and Technologies (IBCAST 2021), Islamabad, 12-16 Jan. 2021.
• S. Liaqat and Gulistan Raja, "Computer-Aided Detection of COVID-19 Using Chest Imaging", 11th International Conference on Pattern Recognition Systems (ICPRS 2021), Curicó, Chile, 17-19 Mar. 2021.
• H. Waris, A.  Ahmad, M. Y. Qadri, Gulistan Raja and T. N. Malik, "GA–EDA: Hybrid Design Space Exploration Engine for Multicore Architecture", Journal of Circuits Systems and Computers, Vol. 30, Issue 10, Aug. 2021, pp. 1-29.
• M. Nawaz, Gulistan Raja and M. Qasim, "A New Approach for Dehazing and Enhancement of Infrared Images", 18th International Bhurban Conference on Applied Sciences and Technologies (IBCAST 2021), Islamabad, 12-16 Jan. 2021.
• L. Khurshid and Gulistan Raja, "Real Time Architecture for Image De-Hazing", 11th International Conference on Pattern Recognition Systems (ICPRS 2021), Curicó, Chile, 17-19 Mar. 2021.
• Ali Haider, Furqan Shaukat, and Junaid Mir, "Human detection in Aerial Thermal imaging using a Fully Convolutional Regression Network," Infrared Physics and Technology, vol. 116, pp. 103796, 2021. (IF=2.997) 
• Naima Aamir, Junaid Mir, Imran Fareed Nizami, Furqan Shaukat, and Muhammad Majid, "HDR-BVQM: High dynamic range blind video quality metric," Multimedia Tools and Applications, vol. 80, pp. 27701–27715, 2021. (IF=2.577
• Ali Ayub Sheikh and Junaid Mir, "Machine Learning Inspired Vision-based Drowsiness Detection using Eye and Body Motion Features," in 13th IEEE International Conference on Information & Communication Technology and System (ICTS), Surabaya, Indonesia, Oct. 2021, pp. 146–150.
• Fahd Siddiq, Muhammad Mansoor Ashraf, and Junaid Mir, "Robust Harmonics Estimation using Hybrid Least Square-based Whale Optimization Algorithm," in 6th Multi-Disciplinary Student Research International Conference (MDSRIC), Wah Cantt, Pakistan, Nov. 2021.
• Majid Riaz, Muhammad Majid, and Junaid Mir, "Emotion Recognition using Electroencephalography in Response to High Dynamic Range Videos," in 10th IEEE International Conference on Information Technology (ICIT), Amman, Jordan, July 2021, pp. 565–570. 
• Majid Riaz, Muhammad Majid, and Junaid Mir, "Emotional Experience Analysis in Response to HDR and SDR content," in 13th IEEE International Conference on Quality of Multimedia Experience (QoMEX), Montreal, Canada, June 2021, pp. 121–124. 
• Soha Salman, Junaid Mir, Muhammad Tallal Farooq, Aneeqa Noor Malik, and Haleemdeen Rizki, "Machine Learning Inspired Efficient Audio Drone Detection using Acoustic Features," in 18th IEEE International Bhurban Conference on Applied Sciences & Technology (IBCAST), Islamabad, Pakistan, Jan. 2021, pp. 335-339. 
• Kabir MS, Mir J, Rascon C, Shahid ML, Shaukat F. Machine learning inspired efficient acoustic gunshot detection and localization system. University of Wah Journal of Computer Science. 2021;3(1).

2020

• Iqra Bibi, Junaid Mir, and Gulistan Raja, "Automated Detection of Diabetic Retinopathy in Fundus Images using Fused Features," Australasian Physical and Engineering Sciences in Medicine,  vol. 43, no. 4, pp. 1253–1264, 2020. (IF=1.430)
• Shahid, Muhammad Laiq Ur Rahman, Junaid Mir, Furqan Shaukat, Muhammad Khurram Saleem, Muhammad Atiq Ur Rehman Tariq, and Ahmed Nouman. "Classification of Pharynx from MRI using a Visual Analysis Tool to Study Obstructive Sleep Apnea." Current Medical Imaging (2020).
• M. Kashif, Gulistan Raja, and F. Shaukat, "An Efficient Content based Image Retrieval System for Diagnosis of Lung Diseases", Journal of Digital Imaging, Vol. 33. Issue 4, Sep. 2020, pp. 971-987.
• M. Usman, U. Zabit, O. D. Bernal, Gulistan Raja, and T. Bosch, "Detection of Multi-Modal Fringes for Self Mixing based Vibration Measurement", IEEE Transactions on Instrument and Measurement, Vol. 69, Issue 1, Jan. 2020, pp. 258-267.
• M. Usman, U. Zabit, O. D. Bernal, and Gulistan Raja, "Blind Identification of Occurrence of Multi-modality in Laser Feedback based Self-Mixing Sensor", Chinese Optics Letters, Vol. 18, Issue 1, Jan. 2020,  pp.  011201- (2020).
• Muhammad Laiq Ur Rahman Shahid, Vladimir Molchanov, Junaid Mir, Furqan Shaukat, and Lars Linsen, "Interactive Visual Analytics Tool for Multidimensional Quantitative and Categorical Data Analysis," Information Visualization, vol. 19, no. 3, 2020. (IF=0.956)

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2019

• Furqan Shaukat, Gulistan Raja, R. Ashraf, S. Khalid, M. Ahmad and A. Ali, “Artificial Neural Network based Classification of Lung Nodules in CT images using Intensity, Shape and Texture Features,” Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 10, pp. 4135-4149, Oct. 2019.
• Furqan Shaukat, K. Javed, Gulistan Raja, Junaid Mir, and Muhammad Laiq-Ur-Rahman Shahid, “Automatic Lung Nodule Detection in CT Images using Convolutional Neural Networks,” IEICE Transactions on Fundamentals on Electronics, Communications and Computer Sciences, vol. E102-A, no.10, pp.1364-1373, Oct. 2019.
• Furqan Shaukat, Gulistan Raja and A. Frangi, “Computer-Aided Detection of Lung Nodules: A Review,” SPIE Journal of Medical Imaging, vol. 6, no. 2, Apr-Jun. 2019.
• M.S. Ahmad, Junaid Mir, M.O. Ullah, Muhammad Laiq-Ur-Rahman Shahid and S.M. Adnan, “An Efficient Heart Murmur Recognition and Cardiovascular Disorders Classification System,” Australasian Physical and Engineering Sciences in Medicine, vol. 42, no. 3, pp. 733-743, 2019.
• R. Chughtai, Gulistan Raja, Junaid Mir and Furqan Shaukat, “An Efficient Scheme for Automatic Pill Recognition Using Neural Networks,” The Nucleus Journal, vol. 56, no. 1, Jun. 2019, pp. 42-48.
• Junaid Mir, D.S. Talagala, A. Fernando and S.S. Hussain, “Improved HEVC λ -domain rate control algorithm for HDR video,” Signal, Image, and Video Processing, vol. 13, no. 3, pp. 439-445, 2019.
• Junaid Mir, D.S. Talagala, A. Fernando and H.K. Arachchi, “A Comprehensive Study and Evaluation of HDR Video Coding,” Arabian Journal of Science and Engineering, vol. 44, no. 3, pp. 2427-2444, 2019.

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2018

• N. U. Ain, Furqan Shaukat, A.S. Nagra and Gulistan Raja, “An Efficient Algorithm for Fingerprint Recognition using Minutiae Extraction,” Pakistan Journal of Science, vol. 70, no.2, Jun. 2018, pp. 169-176.
• Furqan Shaukat and Gulistan Raja, “An Efficient Algorithmic Solution for Automatic Segmentation of Lungs from CT Images,” Pakistan Journal of Science, vol. 70, no. 1, Mar. 2018, pp. 71-78.

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2017

• Furqan Shaukat, Gulistan Raja, A. Gooya and A. Frangi, “Fully Automatic and Accurate Detection of Lung Nodules in CT images using a Hybrid Feature Set,” Medical Physics Journal, vol. 44, no. 7, July 2017, pp. 3615-3629.
• I. Amjad, Furqan Shaukat, Gulistan Raja and A.K. Khan, “An Algorithm to Segment the Mid-Brain Structures Using Multiresolution Non-Rigid Registration,” Pakistan Journal of Science, vol. 69, no. 2, June 2017, pp. 221-227.
• Muhammad Laiq-Ur-Rahman Shahid, Teodora Chitiboi, Tatyana Ivanovska, Vladimir Molchanov, Henry Völzke and Lars Linsen, “Automatic MRI Segmentation of Para-Pharyngeal Fat Pads using Interactive Visual Feature Space Analysis for Classification,” BMC Medical Imaging, vol. 17, no. 1, 2017.

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2016

• Gulistan Raja, M. J. Mirza, and T. Song, “H.264/AVC Deblocking Filter based on Motion Activity in Video Sequences,” Journal of IEICE Electronics Express, Vol. 5, No. 19, 2008, pp. 809-814.
• M. Asghar, I. Arshad, I. A. Taj, Gulistan Raja and A.  K. Khan, “Palm and Finger Segmentation of High Resolution Images using Hand Shape and Texture,” Proceedings of Pakistan Academy of Sciences: A, vol. 53, no. 4, Dec. 2016, pp. 401-416.
• Z. Shabbir, I. Arshad, Gulistan Raja and A. K. Khan, “Content Based Image Retrieval using Improved Local Tetra Pattern and Neural Network,” The Nucleus Journal, vol. 53, no. 4, Dec. 2016, pp. 225-232.
• Z.  Bashir, Gulistan Raja and M. Obaid Ullah, “A Video Enhancement Algorithm for Low-Lighting Environment using FPGA Architecture,” NED University Journal of Research, Vol. XIII, No. 4, Sep. 2016, pp. 81-89.
• B. Hassan, Gulistan Raja, T. Hassan and M. U. Akram, “Structure Tensor Based Automated Detection of Macular Edema and Central Serous Retinopathy using Optical Coherence Tomography Images,” Journal of Optical Society of America A, vol. 33, no. 4, Apr. 2016, pp. 455-463.
• B. Hassan and Gulistan Raja, “Fully Automated Assessment of Macular Edema using Optical Coherence Tomography (OCT) Images,” International Conference on Intelligent Systems Engineering, ICISE 2016, Islamabad, 15-18 Jan. 2016.

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2015

• Tatyana Ivanovska, R. Laqua, Muhammad Laiq-Ur-Rahman Shahid, Lars Linsen, K. Hegenscheid and Henry Völzke, “Automatic Pharynx Segmentation from MRI Data for Analysis of Sleep Related Disorders,” International Journal on Artificial Intelligence Tools, vol. 24, no. 4, 2015.
• Muhammad Laiq-Ur-Rahman Shahid, Teodora Chitiboi, Tatyana Ivanovska, Vladimir Molchanov, Henry Völzke, Horst K. Hahn and Lars Linsen; "Automatic Pharynx Segmentation from MRI Data for Obstructive Sleep Apnea Analysis", Proceedings of 10th  International Conference on Computer Vision Theory and Applications, pp. 599-608, 2015, Berlin, Germany.
• A. Ahmad, I. Arshad and Gulistan Raja, “Partial Fingerprint Image Enhancement using Region Division Technique and Morphological Transform,” The Nucleus Journal, vol. 52, no. 2, June 2015, pp. 63-70.

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Earlier Publications

• I. Arshad, Gulistan Raja and A. K. Khan, “Latent Fingerprints Segmentation: Feasibility of using Clustering based Automation Approach,” Arabian Journal of Science and Engineering, vol. 39, no. 11, Nov. 2014, pp. 7933-7944.
• S. U. Rehman and Gulistan Raja, “Performance Evaluation of HEVC High Efficiency Video Coding over Broadband Networks,” International Journal of Computer Science Issues, Vol. 11, Issue 4, July 2014, pp. 68-74.
• J. A. Raja, Gulistan Raja and A. K. Khan, “Selective Compression of Medical Images using Multiple Regions of Interest,” Life Sciences Journal, vol. 10, no. 9, Sep. 2013, pp. 394-397.
• Muhammad Laiq-Ur-Rahman Shahid and Gulistan Raja, “Implementation of Modified Control Point Image Registration Method,” The Nucleus Journal, vol. 50, no. 1, 2013, pp. 53-60.
• Junaid Mir and Gulistan Raja, “Quad tree Fractal Compression for Brain MRI Images,” The Nucleus Journal, vol. 49, no. 1, 2012.
• Furqan Shoukat and Gulistan Raja, “Implementation of Rule-Based Medical Image Recognition for Lung CT Images,” Proceedings of 9th International Bhurban Conference on Applied Sciences & Technology, Islamabad, 9-12 Jan 2012.
• S. K. Hussain and Gulistan Raja, “A JPEG 2000 based Hybrid Image Compression Technique for Medical Images,” The Nucleus Journal, vol. 48, no. 4, Dec. 2011, pp. 287-293.
• Gulistan Raja, M. J. Mirza, and T. Song, “H.264/AVC Deblocking Filter based on Motion Activity in Video Sequences,” Journal of IEICE Electronics Express, Vol. 5, No. 19, 2008, pp. 809-814.
• Gulistan Raja and M.J. Mirza, “Evaluation of Loop Filtering for Reduction of Blocking Effects in Real Time Low Bit Rate Video Coding,” M.U. Research Journal of Engineering and Technology, Vol. 26, No. 3, 2007, pp. 211-218.

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Research Projects

• BIOMED5.0 “Capacity Building in Biomedical Engineering Education for Digital Transformation and Industry 4.0/5.0 Technologies”

  The project BIOMED5.0 was approved by the CBHE program under Erasmus+ and a grant agreement was signed between the 11-member consortium and the EU for the grant on December 1, 2023

  Lead from UET Taxila:  Prof. Dr. Gulistan Raja, Dr. Furqan Shaukat, and Dr. Junaid Mir

  Amount: €791234/- (1 Million USD)

  Duration: 36 Months

  Summary: BIOMED5.0 aims to establish an international partnership between Pakistani, Irish, and Romanian higher education institutions (HEIs) to modernize and transform Biomedical Engineering (BME) education. BIOMED5.0 objective is to incorporate digital transformation enabling Industry 4.0/5.0 technologies in BME bachelor and master programmes aiming at increasing students’ understanding and knowledge in Industry 4.0/5.0 technologies and to upgrade teaching labs and introduce innovative Virtual Reality, Augmented Reality and Mixed Reality laboratory sessions for enhanced students learning experience. The project aims to foster collaboration between Knowledge Triangle players, promoting innovation and entrepreneurship.

  To achieve these goals, BIOMED5.0 will revise the Programme Educational Objectives and Learning Outcomes for the targeted modules via stakeholder inputs. The project will develop and implement 6 new bachelor and master modules and introduce elements of Industry 4.0/5.0 technologies in 12 existing modules. 5 new micro-credential Open Online Courses (OOC) on Industry 4.0/5.0 will be introduced for professionals as life-long learning. BIOMED5.0 will develop VR/AR/MR based lab training sessions for diagnostic and therapeutic BME devices. The centre will act as a technology incubation hub to drive innovation and support entrepreneurship in BME and healthcare through providing access to fabrication labs and mentorship programmes.

  BIOMED5.0 will benefit approximately 640 academic and 80 lab staff, over 10,000 BME bachelor and master students, 500 working/open to work professionals, and more than 80 entrepreneurs.

  BIOMED5.0 will build the capacity of partner HEIs through trainings in Industry 4.0/5.0 technologies, transformative skills, and mental wellbeing to academic and lab staff, students, and entrepreneurs. BIOMED5.0 will increase awareness and adoption of digital transformation and BIOMED5.0 outcomes through comprehensive dissemination & communication efforts.

• Title: EMeRALDS: Electronic Medical Records driven Automated Lung nodule Detection and cancer risk Stratification

  Summary: Lung cancer has been one of the major threats to human life for decades. With the lowest survival rate following diagnosis, it is the leading cause of cancer deaths worldwide and is a matter of concern in developed and developing countries. Early detection of lung cancer can therefore play a pivotal role in reducing patient mortality. However, detecting lung cancer at an early stage is challenging due to a lack of symptoms in most patients until cancer has advanced to an incurable stage. Current clinical practice in most communities worldwide have limited, or no access to sophisticated computer-assisted detection (CADe) systems and relies on expert radiologists and clinical oncologists for detection and diagnosis. Given the large volume of data in thoracic computed tomography (CT) images (the standard imaging modality for lung cancer screening) and the typically small size of lung nodules, this leads to large intra- and inter-rater variability and a large number of false positives and negatives. This is further compounded in developing countries where the availability of expert radiologists is limited and consequently, misdiagnosis of lung cancer is a major issue. These issues could be resolved with the design of robust, generalizable and scalable CADe systems designed using state-of-the-art machine learning (ML) techniques.

  The main goal of this project is to develop a fully automated, scalable, and robust CADe system for lung nodules for mass screening in developing communities in Pakistan, with limited access to high-quality medical facilities and expert personnel. In the primary phase of the project, we will utilize and extend the current state-of-the-art ML/deep learning (DL) algorithms for lung nodule detection in low-dose CT scans. Following the successful development of the proposed system, it will be deployed in a selected hospital in Pakistan, where its findings on real patient data will be evaluated and compared with clinical experts and a clinically validated tool available on the market. The primary outcome of this project will be a robust nodule detection that can be deployed in a clinical environment in Pakistan to meet current needs.

  Principal Investigator: Dr. Furqan Shaukat

  Co-Principal Investigator: Prof. Dr. Gulistan Raja, Dr. Junaid Mir.

  Amount: 8340150/- (eight million three hundred forty thousand one hundred fifty)

  Duration: 36 Months

• Title: Generic Classification of Scene Contents using Multispectral Imaging, 2020-2021

  Problem Statement: Multispectral Images are suitable for distinguishing between different generic scenes occurring in airborne picture using a multi-spectral camera mounted on Airplane/satellite. Such a capability is useful in autonomous robot applications to help negotiating the environment as well as, e.g. applications intended to create large scale inventories of assets in the proximity of roads. Many materials appearing similar if viewed by a common RGB camera, will show discriminating properties if viewed by a camera capturing a greater number of separated wavelengths. In this project, a GUI using a set of robust algorithms will be developed for broader classification of scene contents. The outcome would be a scene segmented into generic classes e.g. vegetation, concrete, water etc.

  Principal Investigator: Dr. Gulistan Raja

  Amount: PKR 0.20 Million

  Funding Source: RAC

• Title: An Effective Content based Image Retrieval System for Lung Diseases, 2017-2020

  Problem Statement: Existing Content based Image Retrieval Systems (CBIR) mostly perform image retrieval using three fundamental units namely low-level features, similarity measure and semantic gap reduction. The performance of the CBIR system generally depends on feature extraction and similarity measurement technique. The main target of this project is to improve the performance of CBIR system for retrieval of Common imaging signs (CISs) images by using robust combination of multiple descriptors and feature selection scheme to obtain an optimal feature set.

  Principal Investigator: Dr. Gulistan Raja

  Amount: PKR 0.30 Million

  Funding Source: Directorate of ASR&TD UET Taxila

• Title: Automated Detection of Diabetic Retinopathy Using Fundus Images, 2017-2020

  Problem Statement: Diabetic retinopathy (DR) is a severe condition due to diabetes which causes damage to vision and even led to blindness in the patient. DR is one of the most basic reasons for vision loss in the recent-age. If not taken proper measures, this eye problem will increase in future diabetic patients due to diabetes incidence increase in humans. The problem to be addressed in this project is the automated detection between DR and normal images without the need for segmentation.

  Principal Investigator: Dr. Gulistan Raja

  Amount: PKR 0.30 Million

  Funding Source: Directorate of ASR&TD UET Taxila

• Title: Image Enhancement Techniques (Night-Time), 2019-2020

  Problem Statement: Infrared imagery is produced by detecting the radiations emitted by objects. The temperature of objects determines the wavelength of radiation. On the other hand, visible imagery is produced by the rays of light reflected off of objects. This is why, visible images captured in low light or bad weather conditions are especially degraded. Since the amount of light has minimum impact on infrared images, they are effective during the day, at night, and in all weather conditions. This project investigates for the effective method to dehaze and enhance the outdoor infrared images.

  Principal Investigator: Dr. Gulistan Raja

  Amount: PKR 0.10 Million

  Funding Source: RAC

• Title: Brain Activity Analysis in Response to High Dynamic Range (HDR) Content Using Electroencephalography (EEG), 2018-2019.

  Problem Statement: High Dynamic Range (HDR) imaging technologies, the next frontier in digital multimedia, is a step-forward towards the non-trivial challenging tasks of acquisition and representing the high-fidelity representation of the real-world scene on display devices. Through Wide Colour Gamut (WCG) and high contrast, HDR displays can invoke true-to-life visual sensations, which have effect on human perception and brain activity due to closer seamless and immersive real-world experience and needs to be assessed. In this project, human brain activity is analysed in response to HDR and low dynamic range content using electroencephalograph (EEG) signals.

  Principal Investigator: Dr. Junaid Mir

  Amount: PKR 0.47 Million

  Funding Source: Higher Education Commission (HEC), Pakistan.

• Title: Biomedical Signals Classification using Interactive Visual Analysis Tool, 2017-2018.

  Problem Statement: Biomedical signals provide a myriad of information regarding the health of a patient. By examining changes from normal signals, medical experts can determine several different diseases. However, detection of patients from healthy subjects has not been performed using visual analysis tools. The visual analysis approach will help the user to visually analyse the data and get a better understanding of the data. Therefore, automatic classification of the biomedical signals is required.

  Principal Investigator: Dr. Laiq-Ur-Rahman Shahid

  Amount: PKR 0.48 Million

  Funding Source: Higher Education Commission (HEC), Pakistan.

• Title: Upper Airway Segmentation.

  Problem Statement: Over the last few years, the scientists and researchers have put their efforts to study the gravitational effects on the vocal tract of subjects in singing phonation. The analysis method of dynamic processes of the vocal tract (including the oral cavity and pharynx), such as swallowing, singing voice and speech has been improved significantly due to Magnetic Resonance Imaging (MRI). To fully understand upper airway anatomy, we need to examine the volume of the airway and surrounding upper airway structures. To study upper airway anatomy, it is important to develop a segmentation technique to extract the anatomy from medical images. The first step towards this endeavour is to establish a reliable segmentation of the pharynx from 3D MR images. As manual segmentation is a laborious, observer-dependent, and time-consuming process, full automation of the three-dimensional analysis of the pharynx is required.

  Principal Investigator: Dr. Laiq-Ur-Rahman Shahid

• Title: An Efficient Scheme for Lung Nodule Detection, 2015.

  Problem Statement: Lung cancer has been one of the major threats to human life for decades in both developed and under developed countries with the smallest rate of survival after diagnosis. The survival rate can be increased by early nodule detection. Computer Aided Detection (CAD) can be an important tool for early lung nodule detection and preventing the deaths caused by the lung cancer. Current lung nodule detection schemes show their lack of ability to detect all nodules while maintaining the same precision in terms of sensitivity and reduced number of false positives per scan. Most of the algorithms are optimized and limited to a particular set of data which limits the generalization of the results. In addition, the current schemes have not been evaluated on sufficiently large datasets to achieve more robustness. Therefore, methods evaluated having lesser number of nodules are not guaranteed to present the same performance in all circumstances. Moreover, since feature extraction is very important for the characterization of the nodules from other anatomic structures present in the lung region, the choice of optimum feature set for nodule detection via conventional feature-based approaches or convolutional neural networks is still an unresolved issue. Thus, the real challenge is to make more accurate systems in terms of sensitivity and reduced FP/scan with increased nodule diversity.

  Principal Investigator: Dr. Furqan Shaukat

  Amount: PKR 1.74 Million

  Funding Source: Directorate of ASR&TD UET Taxila

Students

Muniba Noreen

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Hafza Eman

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Muhammad Abdullah

Dr. Furqan Shaukat gave a keynote talk on EMeRALDS: Electronic Medical Records driven Automated Lung Nodule Detection and Cancer Risk Stratification at the 26th International Multitopic Conference 2024 (INMIC 2024), held at Salim Habib University, Karachi, from December 30-31, 2024.

Exciting News: Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images" has been accepted for presentation at MLMI 2024 in conjunction with @MICCAI2024 https://lnkd.in/enSCHDEK and the abstract is also accepted for poster presentation at the 110th Annual Meeting of Radiological Society of North America (RSNA) 2024. Paper link: https://link.springer.com/chapter/10.1007/978-3-031-73284-3_8 . This paper is part of the HEC funded NRPU EMeRALDS lead by Dr. Furqan Shaukat.

Our collaborator from Children National Hospital DC, USA, Dr. Anwar, presented the abstract at RSNA, Chicago, USA, held from 01 to 05 December 2024.

Dr. Furqan Shaukat presented the paper, “Fully Automatic Lung Segmentation in Thoracic CT Images using K-means Thresholding" at the 4^th International Conference on Key Enabling Technologies (KEYTECH 2024) held at 1^st -2^nd September 2024 DCU Dublin, Ireland. This paper is part of the HEC funded NRPU EMeRALDS lead by Dr. Furqan Shaukat.

We want to share with much delight that the project BIOMED5.0 (worth 1 Million USD), jointly written by a consortium of Pakistani, Irish, and Romanian higher education institutions (HEIs) to modernize and transform Biomedical Engineering (BME) education in Pakistan, has been approved by the European Commission under the Erasmus+ Program. From UET Taxila, Prof. Dr. Gulistan Raja, Dr. Furqan Shaukat, and Dr. Junaid Mir will be leading the project.