160 citations · 607 across the 73 of their papers we have counts for
12 papers · 1 filter
Stacked Autoencoders for Medical Image Search
S. Sharma, I. Umar, L. Ospina +2
Medical images can be a valuable resource for reliable information to support medical diagnosis. However, the large volume of medical images makes it challenging to retrieve releva…
MinMax Radon Barcodes for Medical Image Retrieval
H. R. Tizhoosh, Shujin Zhu, Hanson Lo +2
Content-based medical image retrieval can support diagnostic decisions by clinical experts. Examining similar images may provide clues to the expert to remove uncertainties in his/…
Learning Opposites Using Neural Networks
Shivam Kalra, Aditya Sriram, Shahryar Rahnamayan +1
Many research works have successfully extended algorithms such as evolutionary algorithms, reinforcement agents and neural networks using "opposition-based learning" (OBL). Two typ…
Radon-Gabor Barcodes for Medical Image Retrieval
Mina Nouredanesh, H. R. Tizhoosh, Ershad Banijamali +1
In recent years, with the explosion of digital images on the Web, content-based retrieval has emerged as a significant research area. Shapes, textures, edges and segments may play…
Barcodes for Medical Image Retrieval Using Autoencoded Radon Transform
Hamid R. Tizhoosh, Christopher Mitcheltree, Shujin Zhu +1
Using content-based binary codes to tag digital images has emerged as a promising retrieval technology. Recently, Radon barcodes (RBCs) have been introduced as a new binary descrip…
Gabor Barcodes for Medical Image Retrieval
Mina Nouredanesh, Hamid R. Tizhoosh, Ershad Banijamali
In recent years, advances in medical imaging have led to the emergence of massive databases, containing images from a diverse range of modalities. This has significantly heightened…
Binary Codes for Tagging X-Ray Images via Deep De-Noising Autoencoders
Antonio Sze-To, Hamid R. Tizhoosh, Andrew K. C. Wong
A Content-Based Image Retrieval (CBIR) system which identifies similar medical images based on a query image can assist clinicians for more accurate diagnosis. The recent CBIR rese…
Anatomy-Aware Measurement of Segmentation Accuracy
Hamid R. Tizhoosh, Ahmed A. Othman
Quantifying the accuracy of segmentation and manual delineation of organs, tissue types and tumors in medical images is a necessary measurement that suffers from multiple problems.…
Generating Binary Tags for Fast Medical Image Retrieval Based on Convolutional Nets and Radon Transform
Xinran Liu, Hamid R. Tizhoosh, Jonathan Kofman
Content-based image retrieval (CBIR) in large medical image archives is a challenging and necessary task. Generally, different feature extraction methods are used to assign express…
Radon Features and Barcodes for Medical Image Retrieval via SVM
Shujin Zhu, H. R. Tizhoosh
For more than two decades, research has been performed on content-based image retrieval (CBIR). By combining Radon projections and the support vector machines (SVM), a content-base…
Evolutionary Projection Selection for Radon Barcodes
Hamid R. Tizhoosh, Shahryar Rahnamayan
Recently, Radon transformation has been used to generate barcodes for tagging medical images. The under-sampled image is projected in certain directions, and each projection is bin…
Tumour ROI Estimation in Ultrasound Images via Radon Barcodes in Patients with Locally Advanced Breast Cancer
Hamid R. Tizhoosh, Mehrdad J. Gangeh, Hadi Tadayyon +1
Quantitative ultrasound (QUS) methods provide a promising framework that can non-invasively and inexpensively be used to predict or assess the tumour response to cancer treatment.…