Multi-class Abnormal Breast Tissue Segmentation Using Texture Features and Analyzing the Growth Factor Using Power Law

B. Monica Jenefer; V. Cyrilraj

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Multi-class Abnormal Breast Tissue Segmentation Using Texture Features and Analyzing the Growth Factor Using Power Law

机译：利用纹理特征进行多类异常乳腺组织分割并使用幂律分析生长因子

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This paper motivated to design and develop an automatic model for multi-class breast tissue segmentation and find the growth of the cancer in breast mammogram images. Various breast tissues are categorized by a novel texture features such as PTPSA-[Piecewise Triangular Prism Surface Area], intensity difference and regular-intensity in mammogram images. Using CRF-[Classical Random Forest] method segmentation and classification of the features can be obtained in mammogram images. The input image feature values are compared to the ground-truth values for confirming the true positive rate of the proposed approach. Efficacy of abnormal breast tissue segmentation is evaluated using publicly available MIAS training dataset.In this paper, we investigate theconsequences of an option, yet just as conceivable, suspicion of tumor growth, to be specific power growth law, acknowledged in direct expand in tumor breadth. We exhibit a simple model for tumor growth, whose global flow demonstrates power law growth of the tumor, much under boundless supplement supply. For corroboration, it is carried out and examined one-, two- and three-dimensional tumor growth tests both in vitro, in MCF-7 cells (breast malignancy cell line) and in vivo, in mouse xenografts.Aftersuccessful tissue segmentation, the growth of the tissue is analyzed using Power Law. Performance evaluation of the proposed approach can be obtained by comparing the simulation output with the ground truth data. The accuracy of the proposed approach reaches up to 97% for MIAS database in term of tumor detection. Also, simulating radiotherapy under power law, Gompertz and exponential tumor growth, it is indicated that the power law model predicts profoundly diverse conclusions for the usually used treatment. This shows the significance of utilizing the proper tumor growth model when computing ideal measurement fractionation plan for radiotherapy.

机译：本文旨在设计和开发用于多类乳房组织分割的自动模型，并在乳房X线照片中发现癌症的生长。乳房的各种组织根据乳腺X线照片中的PTPSA- [Piecewise三角棱镜表面积]，强度差和规则强度等新的纹理特征进行分类。使用CRF- [经典随机森林]方法可以在乳房X光照片中获得特征的分割和分类。将输入图像特征值与真实值进行比较，以确认所提出方法的真实正率。使用公开的MIAS训练数据集评估异常乳腺组织分割的功效。在本文中，我们调查了一种选择的后果，但同样可以想象的是，怀疑肿瘤生长是一种特定的能量生长规律，在肿瘤广度上直接扩大时得到了承认。我们展示了一个简单的肿瘤生长模型，该模型的全局流动证明了幂律生长，这在无穷的补给供应下非常明显。为证实这一点，在小鼠异种移植物中的MCF-7细胞（乳腺癌细胞系）体外和体内进行了一维，二维和三维肿瘤生长测试。使用幂律分析组织的组织。通过将模拟输出与地面真实数据进行比较，可以获得所提出方法的性能评估。就肿瘤检测而言，对于MIAS数据库而言，该方法的准确性高达97％。此外，模拟幂律，Gompertz和指数肿瘤生长下的放疗，表明幂律模型预测了通常使用的治疗方法的深刻结论。这显示了在计算放射治疗的理想测量分级计划时利用适当的肿瘤生长模型的重要性。

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《Biosciences Biotechnology Research Asia》 |2015年第1期|共8页
作者
B. Monica Jenefer; V. Cyrilraj;
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正文语种 eng
中图分类分子生物学;
关键词
Breast Cancer; Mammogram Images; Texture Features; Image Classification; MIAS dataset; Power Law Growth;

机译：乳腺癌;乳房X线照片;纹理特征;图像分类;MIAS数据集;幂律增长;

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1. Multi-class Abnormal Breast Tissue Segmentation Using Texture Features and Analyzing the Growth Factor Using Power Law [J] . B. Monica Jenefer, V. Cyrilraj Biosciences Biotechnology Research Asia . 2015,第1期

机译：利用纹理特征进行多类异常乳腺组织分割并使用幂律分析生长因子
2. Abnormality Segmentation and Classification of Brain MR Images using Combined Edge, Texture Region Features and Radial basics Function [J] . B. Balakumar, P. Raviraj Research journal of applied science, engineering and technology . 2013,第21期

机译：结合边缘，纹理区域特征和径向基函数的脑MR图像异常分割和分类
3. Abnormality Segmentation and Classification of Brain MR Images using Combined Edge, Texture Region Features and Radial basics Function [J] . B. Balakumar, P. Raviraj Research journal of applied science, engineering and technology . 2013,第21期

机译：结合边缘，纹理区域特征和径向基函数的脑MR图像异常分割和分类
4. Multi-class abnormal breast tissue segmentation using texture features [C] . Jenefer B. Monica, Cyrilraj V. 2014 International Conference on Science Engineering and Management Research . 2014

机译：利用纹理特征进行多类异常乳腺组织分割
5. A multidimensional statistical-based texture segmentation algorithm and its application to breast MR images. [D] . Bianchi, Maria Julieta. 1998

机译：基于多维统计的纹理分割算法及其在乳腺MR图像中的应用。
6. Integrated Local Binary Pattern Texture Features for Classificationof Breast Tissue Imaged by Optical Coherence Microscopy [O] . Sunhua Wan, Hsiang-Chieh Lee, Xiaolei Huang, -1

机译：集成的本地二进制图案纹理特征用于分类相干显微镜成像的乳腺组织成像
7. ABSTRACT Various body parts or organs can be analysed to identify the different diseases in the human body. Fingernail analysis is one of the ways to identify disease in the human body. Nails are the body part which are farthest from the heart and therefore receive oxygen at last. As a result the nails are the first who show the symptoms of a disease in the human body. Fingernails can be easily captured for diagnosis and there are no heavy equipment or no specific conditions required to use nail image for disease diagnosis, like in other tests and scanning processes. Human nails deliver beneficial information about complaints or any nutritive imbalances in the human body depending upon their shape, texture and colour. In human beings, numerous systemic and skin diseases can be easily analyzed through careful examination of nails of both the limbs. A lot of nail illnesses have been found to be primary signs of numerous underlying systemic illnesses. The colour, texture or shape changes in nails are signs of many diseases mainly affecting nails. Considering all these properties of nails a system is proposed that uses digital image processing (DIP) methods for identifying such changes in the human nail to get more precise results and predict numerous diseases effortlessly. With the emerging Internet of Things (IOT) concept the generated report is made available remotely, this will help users to reduce transportation efforts. As the system has to deal with large and private data, the security of data must be ensured. To keep the data confidential, the Blockchain concept which is one of the most emerging concepts in the field of data management is used. The paper contains the implementation of the digital image processing for feature extraction of nail images, usage of IOT (ThingSpeak cloud) for data storage and implementation of Blockchain to keep the system secured and theft free. KEY WORDS: Int ernet of thin gs (IOT), Image proc essin g, Thin gSpeak, RG B vavalues, Mean pi xel vavalues, Bloc kchain , Hash key. Disease Diagnostic System: Abnormalities in Human Nail [O] . Pranav S. Wazarkar 2020

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Multi-class Abnormal Breast Tissue Segmentation Using Texture Features and Analyzing the Growth Factor Using Power Law

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