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Cancer benign definition

Published19 Jan Abstract The noninvasive diagnosis of the malignant tumors is an important issue in research nowadays.

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Our purpose is to elaborate computerized, texture-based methods for performing computer-aided characterization and automatic diagnosis of these tumors, using only the information from ultrasound images.

In this paper, we considered some of the most frequent abdominal malignant tumors: the hepatocellular carcinoma and the colonic tumors.

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We compared these structures with the benign tumors and with other visually similar diseases. Besides the textural features that proved in our previous research to be useful in the characterization and recognition of the malignant tumors, we improved our method by using the grey level cooccurrence matrix and the edge orientation cooccurrence matrix of superior order.

As resulted from our experiments, the new textural features increased the malignant tumor classification performance, also revealing visual and physical properties of these structures that emphasized the complex, chaotic structure of the corresponding tissue.

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The colorectal tumors also represent a frequent disease for the population of the developed countries. The golden standard for cancer diagnosis is the biopsy, but this is an invasive, dangerous method that can lead to the spread of the tumor inside the human body.

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A non-invasive, subtle analysis is due, in order to detect the cancer in early evolution stages, when the remedii parazite pentru acnee can be surgically removed. We perform this study by using computerized methods applied on ultrasound images.

Other types of image acquisition techniques, such as computer tomography CTmagnetic resonance imaging MRIand endoscopy are considered invasive or expensive. The texture is an important feature, as it provides subtle information concerning the pathological state of the tissue, overcoming the accuracy of the human perception, through the statistical and multiresolution approaches.

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The texture-based methods in combination with classifiers were widely used in the domain of malignant tumor characterization and recognition from medical images. In [ 2 ], Raeth used the textural features in order to distinguish the normal liver from the diffuse liver diseases and from the malignant liver tumors.

The features derived from the second-order grey levels cooccurrence matrix, from the edge cooccurrence matrix, as well as other edge and gradient-based features, speckle noise distribution parameters, and the Fourier power spectrum, provided satisfying results concerning the differentiation between the tumoral and nontumoral tissue.

Marcos Pretto Mosmann. Natal, RN, Brazil, E-mail: moc. Received Feb 21; Accepted Sep 3. Copyright © Colégio Brasileiro de Radiologia e Diagnóstico por Imagem This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits benign cancer treatment use, distribution, and reproduction in any medium, provided the original benign cancer risks is properly cited.

In [ 3 ] the authors computed the first-order statistics recenzii antihelmintice mean grey level and the grey level variancethe second-order grey level cooccurrence matrix parameters and run-length matrix parameters which were used in combination with an artificial neural networks based classifier, as well as with a classifier based on linear discriminants in order to differentiate the malignant liver tumors from hemangioma and from the normal liver.

The resulted recognition rate was The wavelet transform was also implemented [ 4 ], in order to perform a multi-resolution analysis of the textural features. In [ 5 ] the authors analyzed the fluorescent images of the colonic tissue based cancer benign definition textural parameters derived from the second order grey level cooccurrence matrix GLCMin order to distinguish the colonic healthy mucosa versus adenocarcinoma.

However, a systematic study concerning the most relevant textural features that best characterize the malignant tumors and of the most appropriate methods that lead to an increased diagnosis accuracy is not done.

We perform this in our work by building the imagistic textural model of the malignant tumors. We previously defined the imagistic textural model of the malignant tumors [ 6 ], consisting in the most relevant textural features able to separate the HCC tumor from the visually similar tissues cirrhotic parenchyma, benign tumorstogether with their specific values mean, standard deviation, and probability distribution.

Benign Prostatic Hyperplasia prostate adenoma Cancer benign definition Does hpv virus cause bleeding Tratamentul primar pentru un feocromocitom îl reprezintă intervenția chirurgicală pentru eliminarea tumorii. Papillomavirus et cancer gorge Que significa virus del papiloma humano This condition is known as Benign Prostatic Hyperplasia or prostate adenoma because it refers to the non-malignant growth of prostatic tissue in men from middle age onwards. In a young man, the prostate gland is at an almost undetectable rudimentary stage, and spreads across the coating of the cancer benign definition tract like a cluster of seeds. However, during puberty, its form begins to change due to the increased production of the cancer benign definition and dihydrotestosterone hormones.

In this work, we analyzed new methods for textural features computation, based on the cancer benign definition order grey level cooccurrence matrix GLCM [ 7 ], respectively on the superior order edge orientation cooccurrence matrix EOCMthe purpose being to improve the characterization of the abdominal malignant tumors, and to increase the automatic diagnosis accuracy.

In this way, we expect to get a more subtle evaluation procedure than in the case of using the other textural features. The third-order GLCM was experimented for the analysis of the trabecular bones in proximal femur radiographs [ 8 ], as well as for crop classification [ 9 ], but it was never implemented for tumor characterization and recognition. There are no important realizations in the image analysis domain involving the fifth-order GLCM matrix.

The second order EOCM was implemented by Raeth in [ 2 ] for malignant tumor contour characterization and provided satisfying results in this domain.

The third order EOCM was not previously incubation period of human papillomavirus. Thus, we analyzed the role that the second- third- and fifth-order GLCM, respectively, the second- and third-order EOCM have, concerning both the subtle characterization of HCC and colonic tumor tissue, as well as the automatic diagnosis of these types of cancer.

Extended Haralick features were defined for the characterization of the tumor texture, and the best orientations cancer benign definition the corresponding displacement vectors were determined in cancer benign definition cases of the superior order GLCM and Cancer benign definition. The edge orientation variability feature was also defined in order to characterize the complex structure of the tumor tissue. The malignant tumors were compared with visually similar cancer benign definition.

The HCC tumor was compared with the cirrhotic liver parenchyma on which it had evolved and with the benign liver tumors. The colonic tumors were compared with the inflammatory bowel diseases IBDas they share, in ultrasound images, many visual characteristics with these affections.

The assessment of the relevant textural features for the characterization of the malignant tumors was also performed, through specific methods such as the correlation-based feature selection CFS [ 10 ] and through the evaluation of the individual attributes based on their information gain with respect to the class [ 10 ].

Powerful classifiers that gave the best results in our former experiments [ 6 ], such as the multilayer perceptron [ 11 ] and the support vector machines SVM [ 11 ], as well as the AdaBoost combination scheme [ 11 ], were adopted for the evaluation of the textural model and of the recognition accuracy.

The correlation of the textural features with the internal structure and with the properties of the tumor tissue was also discussed. Materials and Methods 2. Materials and Working Methodology In our study, mainly the patients suffering from HCC and colonic tumors were taken into consideration.

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Patients affected by benign liver tumors such as hemangioma and focal nodular hyperplasia FNH were also considered, being known that these tumors have a similar visual aspect with HCC in many situations. Subjects suffering from inflammatory bowel diseases IBD were taken into account as well, because these affections provided a similar visual aspect of the bowel walls like those provided by the colorectal tumors.

All these patients were previously biopsied. For each patient, multiple images were acquired, corresponding to various orientations of the transducer, using the same settings of the ultrasound machine.

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The same number of images was considered for each patient, as described in the experimental section. B-mode ultrasonography was used, in order to preserve the textural properties of the tissues. Rectangular regions of interest were selected inside the tumors, on the liver tissue, or on the bowel wall, in areas which were not affected by artifacts. Then, the imagistic textural model of the malignant tumors was built according to the steps below, and the role of the new derived textural features in improving the accuracy of the malignant tumor characterization and recognition performance was analyzed.

The Imagistic Textural Model of the Malignant Tumors and the Phases Due for Model Building The imagistic textural model of HCC consists of the set of relevant, independent textural features, able to distinguish this tumor from the cirrhotic liver parenchyma cancer benign definition from the benign tumors. The specific, statistical values of the textural features—mean, standard deviation, and probability distribution—are part of the model. The mathematical description of the imagistic textural model cancer benign definition given below.

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Let be the space of the potentially relevant textural am descoperit că am condiloame, containing a number of such features: The features from are considered in their initial representation, as they appear after applying the image analysis methods.

We define as being the transformed feature space, obtained from the initial feature space,after applying dimensionality reduction methods—mainly feature selection techniques [ 10 ].

The imagistic textural model of the tumor TM consists of a collection of vectorsassociated with each relevant textural featurecontaining the specific values that characterize each analyzed class: The vectors of the imagistic textural model are composed by the specific parameters described by 3where mean the arithmetic mean value and standard deviation are real numbers; the Relevance, represented by an integer, quantifies the importance that the considered textural feature has in the differentiation between HCC and other kinds of tissues.

In order to generate a reliable imagistic textural model, first, the image selection for the training set building is due.

For each considered type of tissue, a corresponding class is built. Then, an image analysis phase is necessary: the textural feature computation using specific methods for texture analysis is involved in this process.

Virusblock prospect values of the textural features are stored in the database and used for further evaluations. The learning phase is essential in order to perform the relevant feature selection, to eliminate the redundant features and to determine the specific, statistical values, and the corresponding probability distributions.

Dimensionality reduction methods consisting of feature selection [ 10 ] and feature extraction techniques [ 11 ] are implemented in this phase. At the end, a validation phase is necessary, involving the evaluation of the generated model by providing the relevant features at the classifiers inputs and estimating the accuracy of cancer benign definition classifier.

A new test set of images, different from the training set, is used in this phase. The phases due in order to build the imagistic textural model are described below. Training Set Building For each patient, three to five images were considered. On each image, rectangular regions of interest were selected on each type of tissue, inside HCC and cancer benign definition colonic tumors, respectively, on the cirrhotic parenchyma on which HCC evolved, as well cancer benign definition inside the benign liver tumors and on the superior part of the bowel wall affected by inflammatory bowel diseases.

Pairs of classes were considered, and then the classes were combined in equal proportions inside the training set. The potentially relevant textural features were determined on the regions of interest, using specific methods for texture analysis, and the corresponding values were stored. An instance of the training set consisted of the values of the considered textural features, computed inside a certain region of interest, followed by the class specification.

Methods Applied during the Image Analysis Phase During the image analysis phase, noise reduction was initially performed, by using an averaging filter [ 12 ]. Then, specific cancer benign definition for texture analysis were applied, providing the initial set of potentially relevant textural features. We previously computed 48 textural features, from the following categories: the mean value of the grey levels [ 12 ], the second order grey levels cooccurrence matrix GLCMand the associated Haralick parameters [ 13 ]—the energy, entropy, correlation, contrast, variance, and local homogeneity that emphasized the global properties of the texture.

Edge and gradient-based statistics [ 12 ], respectively, the frequency and density of the textural microstructures, detected by using the Laws convolution filters were computed as well [ 12 ]. The Shannon entropy [ 14 ], computed after applying the wavelet transform [ 15 ], was also determined.

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The Haar wavelet transform was applied recursively at two levels of resolution: the low-low, low-high, high-low, and high-high components were derived at the first level, then, the wavelet transform was applied again on each of these components. The Shannon entropy was computed on each resulted component, at both first and second levels. The determined textural features were independent on orientation, as they were computed on multiple directions and the result was averaged.

They were also independent of illumination and scaled with the size of the region of interest.

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In this work, we defined and experimented the third-and fifth order GLCM, respectively, the second-and third order EOCM, for obtaining more refined textural features. The effect of the new textural features on the improvement of the imagistic textural model of the malignant tumors was carefully analyzed.

Description of the Learning Phase During the learning phase, the selection of the relevant textural features was performed. We considered a feature as being relevant if it emphasized the defining characteristics of the tumor tissue and it substantially contributed to the separation of the tumor tissue from the visually similar tissues.

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From a more technical point of view, a feature was considered relevant if, by including it in the feature set, it led to an increase in the classification accuracy. There are specific methods for feature selection, integrated in two main groups, filters and wrappers [ 10 ], which perform a reliable separation of the relevant features from the nonrelevant ones.

We compared, in our previous cancer benign definition [ 6 ], various methods from these categories, as well as their combinations. The best results were obtained when using the methods of correlation-based feature selection CFScombined with genetic search [ 10 ], the information gain attribute evaluation cancer benign definition 16 ], the consistency-based feature subset evaluation [ 10 ], respectively the wrapper that used the decision trees as classifier, and the best first search method [ 16 ] for subset finding.

The specific values of the relevant textural features were determined by using confidence intervals and probability distribution cancer benign definition [ 11 ]. In this work, we assessed the relevance of the newly obtained textural features, by using the most powerful feature selection methods, being interested in the diagnosis accuracy improvement. Description of the Validation Phase The validation phase consisted of providing the final set of relevant textural features at the inputs of some powerful classifiers, and in analyzing their effect on the classification process improvement.

Classifiers from different categories, as well as classifier combinations, were compared in order to obtain the best performance humani papiloma virus simptomi kod muskaraca this phase [ 6 ]. The best results were provided by the methods of support vector machines SVM [ 11 ] with polynomial kernel of 3rd degree, by the multilayer perceptron MLPdecision trees C4.

The following parameters were used in order to assess the classification performance: the recognition rate percent of correctly classified instancesthe sensitivity TP ratethe specificity TN ratethe area under the ROC curve AUC [ 11 ], and the time due for model building [ 16 ].

The stratified cross-validation strategy [ 11 ] was implemented for classification performance evaluation, in order to preserve the original class proportions. Julesz et al. Haralick [ 18 ] defined the two-dimensional cooccurrence matrix of the grey levels as containing, in its elements, the number of pairs of cancer benign definition having two specific values of the intensity, andbeing situated at a distance defined by a displacement vector: Haralick also defined and implemented statistical measures, such as the homogeneity, energy, entropy, correlation, variance, contrast [ 18 ], in order to emphasize the global properties of the texture.

In [ 7 ], Akono et al. He also extended the mathematical expressions of several statistical Haralick measures from order two to ordersuch as the sum of the GLCM elements, the inverse difference, the dissimilarity and the contrast.

We defined the GLCM of order in the following manner:.