{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/quantification-of-ultrasonic-texture","title":"Quantification of Ultrasonic Texture heterogeneity via Volumetric Stochastic Modeling for Tissue Characterization","arxiv_id":"1601.03531","date":"2016-01-14","proceeding":null,"authors":["O. S. Al-Kadi","Daniel Y. F. Chung","Robert C. Carlisle","Constantin C. Coussios","J. Alison Noble"],"abstract":"Intensity variations in image texture can provide powerful quantitative\ninformation about physical properties of biological tissue. However, tissue\npatterns can vary according to the utilized imaging system and are\nintrinsically correlated to the scale of analysis. In the case of ultrasound,\nthe Nakagami distribution is a general model of the ultrasonic backscattering\nenvelope under various scattering conditions and densities where it can be\nemployed for characterizing image texture, but the subtle intra-heterogeneities\nwithin a given mass are difficult to capture via this model as it works at a\nsingle spatial scale. This paper proposes a locally adaptive 3D\nmulti-resolution Nakagami-based fractal feature descriptor that extends\nNakagami-based texture analysis to accommodate subtle speckle spatial frequency\ntissue intensity variability in volumetric scans. Local textural fractal\ndescriptors - which are invariant to affine intensity changes - are extracted\nfrom volumetric patches at different spatial resolutions from voxel\nlattice-based generated shape and scale Nakagami parameters. Using ultrasound\nradio-frequency datasets we found that after applying an adaptive fractal\ndecomposition label transfer approach on top of the generated Nakagami voxels,\ntissue characterization results were superior to the state of art. Experimental\nresults on real 3D ultrasonic pre-clinical and clinical datasets suggest that\ndescribing tumor intra-heterogeneity via this descriptor may facilitate\nimproved prediction of therapy response and disease characterization.","url_abs":"http://arxiv.org/abs/1601.03531v1","url_pdf":"http://arxiv.org/pdf/1601.03531v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"texture-classification","task_name":"Texture Classification"}],"methods":[],"datasets_introduced":[{"slug":"radio-freqency-ultrasound-volume-dataset-for","name":"Radio-Freqency Ultrasound volume dataset for pre-clinical liver tumors","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}