Spatiotemporal sharpening of sub-pixel super-resolution by means of two infrared spectrum cameras for early cancer detection

Lee, Chia-Yen; Hsieh, Hsin-Yu; Lee, Si‐Chen; Huang, Chiun‐Sheng; Chang, Yeun‐Chung; Chen, Chung-Ming; Szu, Harold

doi:10.1117/12.793385

Cited by 3 publications

(1 citation statement)

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“…Cancerous tissues tend to have a higher temperature signature than surrounding normal tissues [1] and infrared image has been widely used and suggested in medical applications as a potential assessment tool for breast tumor detection and for the high-temperature tumor generating heat areas on infrared images [2]. If longitudinal breast IR images in the spatiotemporal domain are aligned well, the chemotherapy response could be evaluated and even tumors might be identified by the change of heat areas [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Modified Harris Corner Detection for Breast IR Image

Lee

Wang

Chen

et al. 2014

Mathematical Problems in Engineering

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Harris corner detectors, which depend on strong invariance and a local autocorrelation function, display poor detection performance for infrared (IR) images with low contrast and nonobvious edges. In addition, feature points detected by Harris corner detectors are clustered due to the numerous nonlocal maxima. This paper proposes a modified Harris corner detector that includes two unique steps for processing IR images in order to overcome the aforementioned problems. Image contrast enhancement based on a generalized form of histogram equalization (HE) combined with adjusting the intensity resolution causes false contours on IR images to acquire obvious edges. Adaptive nonmaximal suppression based on eliminating neighboring pixels avoids the clustered features. Preliminary results show that the proposed method can solve the clustering problem and successfully identify the representative feature points of IR breast images.

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Section: Introductionmentioning

confidence: 99%

A Modified Harris Corner Detection for Breast IR Image

Lee

Wang

Chen

et al. 2014

Mathematical Problems in Engineering

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Image registration method using representative feature detection and iterative coherent spatial mapping for infrared medical images with flat regions

Wang

Lee

Lai

et al. 2022

Sci Rep

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In the registration of medical images, nonrigid registration targets, images with large displacement caused by different postures of the human body, and frequent variations in image intensity due to physiological phenomena are substantial problems that make medical images less suitable for intensity-based image registration modes. These problems also greatly increase the difficulty and complexity of feature detection and matching for feature-based image registration modes. This research introduces an automatic image registration algorithm for infrared medical images that offers the following benefits: effective detection of feature points in flat regions (cold patterns) that appear due to changes in the human body’s thermal patterns, improved mismatch removal through coherent spatial mapping for improved feature point matching, and large-displacement optical flow for optimal transformation. This method was compared with various classical gold standard image registration methods to evaluate its performance. The models were compared for the three key steps of the registration process—feature detection, feature point matching, and image transformation—and the results are presented visually and quantitatively. The results demonstrate that the proposed method outperforms existing methods in all tasks, including in terms of the features detected, uniformity of feature points, matching accuracy, and control point sparsity, and achieves optimal image transformation. The performance of the proposed method with four common image types was also evaluated, and the results verify that the proposed method has a high degree of stability and can effectively register medical images under a variety of conditions.

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