arXiv 1503.063232015-03-21cs.CV

Wavelet based approach for tissue fractal parameter measurement: Pre cancer detection

Indian Institute of Science Education and Research (IISER) Kolkata（印度科学教育与研究所加尔各答分校）；Institute of Engineering & Management, Kolkata（工程与管理学院（加尔各答））；Indian Institute of Technology (IIT) Kanpur（印度理工学院坎普尔分校）；College of Engineering & Management, Kolaghat（工程与管理学院（科拉加特））

Sabyasachi Mukhopadhyay, Nandan K. Das, Soham Mandal, Sawon Pratiher, Asish Mitra, Asima Pradhan, Nirmalya Ghosh, Prasanta K. Panigrahi

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

In this paper, we have carried out the detail studies of pre-cancer by wavelet coherency and multifractal based detrended fluctuation analysis (MFDFA) on differential interference contrast (DIC) images of stromal region among different grades of pre-cancer tissues. Discrete wavelet transform (DWT) through Daubechies basis has been performed for identifying fluctuations over polynomial trends for clear characterization and differentiation of tissues. Wavelet coherence plots are performed for identifying the level of correlation in time scale plane between normal and various grades of DIC samples. Applying MFDFA on refractive index variations of cervical tissues, we have observed that the values of Hurst exponent (correlation) decreases from healthy (normal) to pre-cancer tissues. The width of singularity spectrum has a sudden degradation at grade-I in comparison of healthy (normal) tissue but later on it increases as cancer progresses from grade-II to grade-III.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours

University of Sussex（苏塞克斯大学）

With the heterogeneous nature of tissue texture, using a single resolution approach for optimum classification might not suffice. In contrast, a multiresolution wavelet packet analysis can decompose the input signal into a set of frequency subbands giving the opportunity to characterise the texture at the appropriate frequency channel. An adaptive best bases algorithm for optimal bases selection for meningioma histopathological images is proposed, via applying the fractal dimension (FD) as the bases selection criterion in a tree-structured manner. Thereby, the most significant subband that bet…

2016-01-02cs.CV

Gabor wavelets combined with volumetric fractal dimension applied to texture analysis

University of São Paulo（圣保罗大学）

Texture analysis and classification remain as one of the biggest challenges for the field of computer vision and pattern recognition. On this matter, Gabor wavelets has proven to be a useful technique to characterize distinctive texture patterns. However, most of the approaches used to extract descriptors of the Gabor magnitude space usually fail in representing adequately the richness of detail present into a unique feature vector. In this paper, we propose a new method to enhance the Gabor wavelets process extracting a fractal signature of the magnitude spaces. Each signature is reduced usin…

2014-12-25cs.CV

Fast Estimation of Haemoglobin Concentration in Tissue Via Wavelet Decomposition

Tissue oxygenation and perfusion can be an indicator for organ viability during minimally invasive surgery, for example allowing real-time assessment of tissue perfusion and oxygen saturation. Multispectral imaging is an optical modality that can inspect tissue perfusion in wide field images without contact. In this paper, we present a novel, fast method for using RGB images for MSI, which while limiting the spectral resolution of the modality allows normal laparoscopic systems to be used. We exploit the discrete Haar decomposition to separate individual video frames into low pass and directio…

2017-06-22cs.CV

Heterogeneous tissue characterization using ultrasound: a comparison of fractal analysis backscatter models on liver tumors

University of Oxford（牛津大学）；University of Jordan（约旦大学）

Assessing tumor tissue heterogeneity via ultrasound has recently been suggested for predicting early response to treatment. The ultrasound backscattering characteristics can assist in better understanding the tumor texture by highlighting local concentration and spatial arrangement of tissue scatterers. However, it is challenging to quantify the various tissue heterogeneities ranging from fine-to-coarse of the echo envelope peaks in tumor texture. Local parametric fractal features extracted via maximum likelihood estimation from five well-known statistical model families are evaluated for the …

2019-12-20cs.CVeess.IV

深度多分辨率分形纹理的计算建模及其在异常脑组织分割中的应用

Computational Modeling of Deep Multiresolution-Fractal Texture and Its Application to Abnormal Brain Tissue Segmentation

Old Dominion University（奥多明尼昂大学）；National Laboratory for Cancer Research（国家癌症研究实验室）

提出融合多分辨率 fBm 与深度多分辨率分析的三维 MFDNN，用于从脑 MRI 学习分形纹理特征，并在 BRATS 2021 的 1251 例病例上实现更优或可比的肿瘤分割性能。

2023-06-07cs.CVcs.LGeess.IV

Quantification of Ultrasonic Texture heterogeneity via Volumetric Stochastic Modeling for Tissue Characterization

University of Oxford（牛津大学）；University of Jordan（约旦大学）

Intensity variations in image texture can provide powerful quantitative information about physical properties of biological tissue. However, tissue patterns can vary according to the utilized imaging system and are intrinsically correlated to the scale of analysis. In the case of ultrasound, the Nakagami distribution is a general model of the ultrasonic backscattering envelope under various scattering conditions and densities where it can be employed for characterizing image texture, but the subtle intra-heterogeneities within a given mass are difficult to capture via this model as it works at…

2016-01-14cs.CV

更多相关论文收起更多论文

从正确图像块学习：面向病理组织学表征学习的两阶段小波驱动掩码自编码器

Learning from the Right Patches: A Two-Stage Wavelet-Driven Masked Autoencoder for Histopathology Representation Learning

PLRI Medical Informatics Institute（PLRI医学信息学研究所）；CAIMed Reserch Center（CAIMed研究中心）；Hannover Medical School（汉诺威医学院）；Computer Science Institute（计算机科学研究所）；Leibniz University Hannover（莱比锡大学汉诺威分校）；Sanford Burnham Prebys Medical Discovery Institute（桑福医疗发现研究所）；Rady Children’s Institute for Genomic Medicine（拉迪儿童基因医学研究所）；University of California San Diego（加州大学圣地亚哥分校）

针对病理MAE预训练随机采样引入噪声的问题，提出两阶段小波驱动的WISE-MAE框架，模拟病理诊断流程筛选有效图像块，在多癌症数据集上实现高效优质的弱监督表征与分类性能。

2025-11-19cs.CV

A Multiresolution Clinical Decision Support System Based on Fractal Model Design for Classification of Histological Brain Tumours

University of Oxford（牛津大学）

Tissue texture is known to exhibit a heterogeneous or non-stationary nature, therefore using a single resolution approach for optimum classification might not suffice. A clinical decision support system that exploits the subband textural fractal characteristics for best bases selection of meningioma brain histopathological image classification is proposed. Each subband is analysed using its fractal dimension instead of energy, which has the advantage of being less sensitive to image intensity and abrupt changes in tissue texture. The most significant subband that best identifies texture discon…

2015-12-25cs.CV

MS-GWNN:multi-scale graph wavelet neural network for breast cancer diagnosis

Peking University（北京大学）；Beijing Institute of Big Data Research（北京大数据研究院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）

Breast cancer is one of the most common cancers in women worldwide, and early detection can significantly reduce the mortality rate of breast cancer. It is crucial to take multi-scale information of tissue structure into account in the detection of breast cancer. And thus, it is the key to design an accurate computer-aided detection (CAD) system to capture multi-scale contextual features in a cancerous tissue. In this work, we present a novel graph convolutional neural network for histopathological image classification of breast cancer. The new method, named multi-scale graph wavelet neural ne…

2020-12-29cs.AIcs.CV

POWDR: 基于小波扩散的病理保持3D MRI外推

POWDR: Pathology-preserving Outpainting with Wavelet Diffusion for 3D MRI

GE HealthCare（GE健康护理）

POWDR通过小波扩散模型生成病理保持的3D MRI外推数据，解决医学影像数据稀缺和类别不平衡问题，提升肿瘤分割性能。

2026-01-14cs.CVeess.IV

Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform

École de Technologie Supérieure, Université du Québec（魁北克大学高等技术学院）

Breast cancer is a health problem that affects mainly the female population. An early detection increases the chances of effective treatment, improving the prognosis of the disease. In this regard, computational tools have been proposed to assist the specialist in interpreting the breast digital image exam, providing features for detecting and diagnosing tumors and cancerous cells. Nonetheless, detecting tumors with a high sensitivity rate and reducing the false positives rate is still challenging. Texture descriptors have been quite popular in medical image analysis, particularly in histopath…

2022-02-27cs.CVcs.LGeess.IV

具有双重分形特征的指数像素化积分变换用于增强胸部X射线异常检测

Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection

The Construct（建构研究院）；Involgixs Inc（Involgixs公司）；Vellore Institute of Technology（韦洛尔理工学院）

针对胸部X射线低对比度等检测难题，提出指数像素化积分变换结合双重分形特征与多元自适应回归样条分类，在基准数据集上实现高准确率（98.46%-99.45%）和F1分数（96.53%-98.10%）。

2026-09-11cs.CVeess.IV

Deep Autoencoders for Anomaly Detection in Textured Images using CW-SSIM

Politecnico di Milano（米兰理工大学）

Detecting anomalous regions in images is a frequently encountered problem in industrial monitoring. A relevant example is the analysis of tissues and other products that in normal conditions conform to a specific texture, while defects introduce changes in the normal pattern. We address the anomaly detection problem by training a deep autoencoder, and we show that adopting a loss function based on Complex Wavelet Structural Similarity (CW-SSIM) yields superior detection performance on this type of images compared to traditional autoencoder loss functions. Our experiments on well-known anomaly …

2022-08-30cs.CVcs.LG

Detection and classification of masses in mammographic images in a multi-kernel approach

Federal University of Pernambuco（伯南布哥联邦大学）

According to the World Health Organization, breast cancer is the main cause of cancer death among adult women in the world. Although breast cancer occurs indiscriminately in countries with several degrees of social and economic development, among developing and underdevelopment countries mortality rates are still high, due to low availability of early detection technologies. From the clinical point of view, mammography is still the most effective diagnostic technology, given the wide diffusion of the use and interpretation of these images. Herein this work we propose a method to detect and cla…

2017-12-20cs.CVcs.NEeess.IV