Feature Enhancement in Medical Ultrasound Videos Using Contrast-Limited Adaptive Histogram Equalization.
Speckle noise reduction algorithms are extensively used in the field of ultrasound image analysis with the aim of improving image quality and diagnostic accuracy. However, significant speckle filtering induces blurring, and this requires the enhancement of features and fine details. We propose a nov...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 1; pp. 273 - 286 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142164504&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142164504 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2020 vid: 33 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142164504 142164504 142164504 10.1007/s10278-019-00211-5 142164504 ppf: 273 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Feature Enhancement in Medical Ultrasound Videos Using Contrast-Limited Adaptive Histogram Equalization. aug: au: Singh, Prerna Mukundan, Ramakrishnan De Ryke, Rex affil: Computer Science and Software Engineering, College of Engineering, University of Canterbury, 20 Kirkwood Ave, Upper Riccarton, 8041, Christchurch, New Zealand sug: subj: Algorithms Evaluation Ultrasonography Trends Image Enhancement Enhancement of Contrast Effect Human Conceptual Framework Neural Networks (Computer) Image Processing, Computer Assisted Comparative Studies ab: Speckle noise reduction algorithms are extensively used in the field of ultrasound image analysis with the aim of improving image quality and diagnostic accuracy. However, significant speckle filtering induces blurring, and this requires the enhancement of features and fine details. We propose a novel framework for both multiplicative noise suppression and robust contrast enhancement and demonstrate its effectiveness using a wide range of clinical ultrasound scans. Our approach to noise suppression uses a novel algorithm based on a convolutional neural network that is first trained on synthetically modeled ultrasound images and then applied on real ultrasound videos. The feature improvement stage uses an improved contrast-limited adaptive histogram equalization (CLAHE) method for enhancing texture features, contrast, resolvable details, and image structures to which the human visual system is sensitive in ultrasound video frames. The proposed CLAHE algorithm also considers an automatic system for evaluating the grid size using entropy, and three different target distribution functions (uniform, Rayleigh, and exponential), and interpolation techniques (B-spline, cubic, and Lanczos-3). An extensive comparative study has been performed to find the most suitable distribution and interpolation techniques and also the optimal clip limit for ultrasound video feature enhancement after speckle suppression. Subjective assessments by four radiologists and experimental validation using three quality metrics clearly indicate that the proposed framework generates superior performance compared with other well-established methods. The processing pipeline reduces speckle effectively while preserving essential information and enhancing the overall visual quality and therefore could find immediate applications in real-time ultrasound video segmentation and classification algorithms. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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