scholarly journals Identification of Pancreaticoduodenectomy Resection for Pancreatic Head Adenocarcinoma: A Preliminary Study of Radiomics

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Bei Hui ◽  
Jia-Jun Qiu ◽  
Jin-Heng Liu ◽  
Neng-Wen Ke
Keyword(s):  
Wavelet Transform ◽  
Cancer Cells ◽  
Resection Margin ◽  
Pancreatic Head ◽  
Texture Features ◽  
Pathological Examination ◽  
Resection Margins ◽  
Support Vector ◽  
Svm Classifier ◽  
Preoperative Ct

Background. In a pathological examination of pancreaticoduodenectomy for pancreatic head adenocarcinoma, a resection margin without cancer cells in 1 mm is recognized as R0; a resection margin with cancer cells in 1 mm is recognized as R1. The preoperative identification of R0 and R1 is of great significance for surgical decision and prognosis. We conducted a preliminary radiomics study based on preoperative CT (computer tomography) images to evaluate a resection margin which was R0 or R1. Methods. We retrospectively analyzed 258 preoperative CT images of 86 patients (34 cases of R0 and 52 cases of R1) who were diagnosed as pancreatic head adenocarcinoma and underwent pancreaticoduodenectomy. The radiomics study consists of five stages: (i) delineate and segment regions of interest (ROIs); (ii) by solving discrete Laplacian equations with Dirichlet boundary conditions, fit the ROIs to rectangular regions; (iii) enhance the textures of the fitted ROIs combining wavelet transform and fractional differential; (iv) extract texture features from the enhanced ROIs combining wavelet transform and statistical analysis methods; and (v) reduce features using principal component analysis (PCA) and classify the resection margins using the support vector machine (SVM), and then investigate the associations between texture features and histopathological characteristics using the Mann–Whitney U-test. To reduce overfitting, the SVM classifier embedded a linear kernel and adopted the leave-one-out cross-validation. Results. It achieved an AUC (area under receiver operating characteristic curve) of 0.8614 and an accuracy of 84.88%. Setting p≤0.01 in the Mann–Whitney U-test, two features of the run-length matrix, which are derived from diagonal sub-bands in wavelet decomposition, showed statistically significant differences between R0 and R1. Conclusions. It indicates that the radiomics study is rewarding for the aided diagnosis of R0 and R1. Texture features can potentially enhance physicians’ diagnostic ability.

scholarly journals A computer-aided diagnosis system based on computed tomography images of pancreatic head adenocarcinoma applied for identifying pancreatoduodenectomy resection

2019 ◽  
Author(s):  
Bei Hui ◽  
Jia-Jun Qiu ◽  
Jin-Heng Liu ◽  
Neng-Wen Ke
Keyword(s):  
Wavelet Transform ◽  
Cancer Cells ◽  
Pancreatic Head ◽  
Texture Features ◽  
Computer Aided Diagnosis ◽  
Pathological Examination ◽  
Resection Margins ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

Abstract Background: In a pathological examination of pancreaticoduodenectomy for pancreatic head adenocarcinoma, resection margins have no cancer cells within 1 mm, the resection is considered as R0 resection; resection margins have cancer cells within 1 mm, the resection is recognized as R1 resection. The pathological examinations of the resection margins are complicated and depend on the subjective experiences of physicians to some extent. This study aims to design a computer-aided diagnosis (CAD) system based on texture features of preoperative computer tomography (CT) images to evaluate a resection margin was R0 or R1.Methods: This study retrospectively analyzed 86 patients who were diagnosed as pancreatic head adenocarcinoma by preoperative abdominal CT examination. These patients underwent pancreaticoduodenectomies, then their resection margins were pathologically diagnosed as R0 or R1. The CAD system consists of five stages: (i) delineate and segment regions of interest (ROIs); (ii) by solving discrete Laplacian equations with Dirichlet boundary conditions, fit ROIs to rectangular regions; (iii) enhance textures of ROIs combining wavelet transform and fractional differential; (iv) extract texture features combining wavelet transform and statistical analysis methods; (v) reduce features using principal component analysis (PCA) and perform classification using support vector machine (SVM), use a linear kernel function and leave-one-out cross-training and testing to reduce overfitting. Mann-Whitney U-test is used to explore associations between texture features and histopathological characteristics.Results: The developed CAD system achieved an AUC (area under receiver operating characteristic curve) of 0.8614 and an accuracy of 84.88%. Setting p-value ≤ 0.01 in the Mann-Whitney U-test, two features of run-length matrix, which derived from diagonal subbands in wavelet decomposition, showed statistically significant differences between R0 and R1.Conclusions: It indicates that the developed CAD system is rewarding for discriminating R0 from R1. Texture features can potentially enhance physicians' diagnostic ability.

scholarly journals Radiomics Signatures of Computed Tomography Imaging for Predicting Resection Margin Status in Pancreatic Head Adenocarcinoma

2020 ◽  
Author(s):  
Jinheng Liu ◽  
Xubao Liu ◽  
Jiajun Qiu ◽  
Yanting Wang ◽  
Wei Huang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Wavelet Transform ◽  
Resection Margin ◽  
Pancreatic Head ◽  
Texture Features ◽  
Margin Status ◽  
Pathological Examination ◽  
Gray Level ◽  
Survival Prognosis ◽  
Resection Margin Status

Abstract Background: To identify preoperative computed tomography radiomics texture features which correlate with resection margin status and prognosis in resected pancreatic head adenocarcinoma. Methods: Improved prognostication methods utilizing novel non-invasive radiomic techniques may accurately predict resection margin status preoperatively. In an ongoing concerning pancreatic head adenocarcinoma, the venous enhanced CT images of 86 patients who underwent pancreaticoduodenectomy were selected, and the resection margin (>1 mm or ≤1 mm) was identified by pathological examination. Three regions of interests (ROIs) were then taken from superior to inferior facing the superior mesenteric vein and artery. Subsequent Laplacian-Dirichlet based texture analysis methods extracting algorithm flows of texture features within ROIs were analyzed and assessed in relation to patient prognosis.Results: Patients with >1 mm resection margin had an overall improved survival compared to ≤1 mm (P < 0.05). Distance 1 and 2 of Gray level co-occurrence matrix, high Gray-level run emphasis of run-length matrix and average filter of wavelet transform (all P < 0.05) were correlated with resection margin status (Area under the curve was 0.784, sensitivity was 75% and specificity was 79%). The energy of wavelet transform, the measure of smoothness of histogram and the variance in 2 direction of Gabor transform are independent predictors of overall survival prognosis, independent of resection margin.Conclusions: Resection margin status (>1 mm vs ≤1 mm) is a key prognostic factor in pancreatic adenocarcinoma and CT radiomic analysis have the potential to predict resection margin status preoperatively, and the radiomic labels may improve selection neoadjucant therapy. Trial registration: Clinicaltrials.gov/ct2/show/NCT02928081.

scholarly journals Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

2020 ◽  
Vol 9 (2) ◽  
pp. 109 ◽  
Author(s):  
Bo Cheng ◽  
Shiai Cui ◽  
Xiaoxiao Ma ◽  
Chenbin Liang
Keyword(s):  
High Resolution ◽  
Extraction Method ◽  
Texture Features ◽  
Decomposition Methods ◽  
Projection Matrix ◽  
Training Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Building Area ◽  
Adaptive Neighborhood

Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity. Therefore, to find intrinsic features for target recognition, a building area extraction method for PolSAR images based on the Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) algorithm is proposed. First, 52 features are extracted by using the Gray level co-occurrence matrix (GLCM) and five polarization decomposition methods. The feature set is divided into 20 dimensions, 36 dimensions, and 52 dimensions. Next, the ANSNPE algorithm is applied to the training samples, and the projection matrix is obtained for the test image to extract the new features. Lastly, the Support Vector machine (SVM) classifier and post processing are used to extract the building area, and the accuracy is evaluated. Comparative experiments are conducted using Radarsat-2, and the results show that the ANSNPE algorithm could effectively extract the building area and that it had a better generalization ability; the projection matrix is obtained using the training data and could be directly applied to the new sample, and the building area extraction accuracy is above 80%. The combination of polarization and texture features provide a wealth of information that is more conducive to the extraction of building areas.

scholarly journals Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1443
Author(s):  
Mai Ramadan Ibraheem ◽  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Mohammed Elmogy
Keyword(s):  
Area Under The Curve ◽  
Malignant Neoplasm ◽  
Texture Features ◽  
Feature Space ◽  
Skin Neoplasms ◽  
Support Vector ◽  
Svm Classifier ◽  
Abnormal Growth ◽  
Malignant Skin ◽  
High Level

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

Content-based image retrieval based on supervised learning and statistical-based moments

2019 ◽  
Vol 33 (19) ◽  
pp. 1950213 ◽  
Author(s):  
Vibhav Prakash Singh ◽  
Rajeev Srivastava ◽  
Yadunath Pathak ◽  
Shailendra Tiwari ◽  
Kuldeep Kaur
Keyword(s):  
Image Retrieval ◽  
Supervised Learning ◽  
Texture Features ◽  
Ground Truth ◽  
Local Binary Patterns ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Approaches ◽  
Query Image

Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.

Clinical outcomes of positive resection margins after endoscopic resection of early colorectal cancer.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 688-688
Author(s):  
Jin-Oh Kim
Keyword(s):  
Cancer Cells ◽  
Clinical Outcomes ◽  
Cancer Cell ◽  
Endoscopic Resection ◽  
Resection Margin ◽  
Early Colorectal Cancer ◽  
Resection Margins ◽  
Positive Resection Margin ◽  
Positive Group ◽  
Positive Resection Margins

688 Background: The management of patients with a positive resection margin after endoscopic resection of early colorectal cancer (ECC) depends on various clinical factors, including the pathology. There is little information on the clinical outcomes according to the subsequent management of a positive resection margin in patients with ECC treated by endoscopic resection. We assessed the management according to the pathology of the positive margin and evaluated the clinical outcomes. Methods: Consecutive patients with ECC who underwent endoscopic resection from January 2004 to December 2014 were reviewed. This study retrospectively analyzed 363 lesions from 338 patients (mean age, 60.1 years; 68% [230/338] male). Results: The resection margin was positive in 29.2% of patients, including cancer cells in 9.9%, adenoma in 16.5%, and high-grade dysplasia (HGD) in 2.8%. Subsequent surgery was performed on 11.8% of patients, 72.2% (26/43) of whom were cancer cell–positive, while 23.3% (10/43) were resection margin–negative but had deep submucosal (SM) or lymphatic invasion. Remnant cancer cells were identified in 25.6% (11/43) of the operated group and 81.8% (9/11) of the cancer cell–positive group. On early follow-up surveillance colonoscopy (mean interval, 3.57 months) in 88.2% of patients (320/363), including 95.7% (67/70) of the adenoma and HGD-positive group, only one (0.3%, 1/320) case of remnant adenoma was found. In the multivariate analysis, deep SM invasion ( p=0.026), number of pieces of piecemeal resection (p=0.03) and cancer cell positivity ( p=0.001) predicted subsequent surgery. In the multivariate analysis, an endoscopic appearance of incomplete resection ( p=0.002) and cancer cell positivity (p=0.041) were related to the identification of remnant cancer cells after subsequent surgery. Conclusions: Patients with an adenoma-positive resection margin had favorable clinical outcomes during subsequent surveillance. The choice of subsequent surgery was related to deep SM invasion and cancer cell–positive resection margins, and subsequent surgery group showed a high rate of remnant cancer cells.

scholarly journals Classification for Liver Diseases Based on Ultrasound Image Texture Features

2018 ◽  
Author(s):  
Sendren Sheng-Dong Xu ◽  
Chien-Tien Su ◽  
Chun-Chao Chang ◽  
Pham Quoc Phu
Keyword(s):  
Liver Abscess ◽  
Liver Diseases ◽  
Ultrasound Image ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Svm Classifier ◽  
Ultrasound Images ◽  
Gray Level ◽  
Forward Selection

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

scholarly journals Detection of Epileptic Seizure Using Wavelet Analysis based Shannon Entropy, Logarithmic Energy Entropy and Support Vector Machine

2018 ◽  
Vol 7 (4.10) ◽  
pp. 935
Author(s):  
Vasudha Harlalka ◽  
Viraj Pradip Puntambekar ◽  
Kalugotla Raviteja ◽  
P. Mahalakshmi
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Shannon Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Complex Wavelet Transform ◽  
Energy Entropy ◽  
Electroencephalogram Eeg ◽  
Logarithmic Energy

Epilepsy is a prevalent condition, mainly affecting the nervous system of the human body. Electroencephalogram (EEG) is used to evaluate and examine the seizures caused due to epilepsy. The issue of low precision and poor comprehensiveness is worked upon using dual tree- complex wavelet transform (DT-CWT), rather than discrete wavelet transform (DWT). Here, Logarithmic energy entropy (LogEn) and Shannon entropy (ShanEn) are taken as input features. These features are fed to Linear Support Vector Machine     (L-SVM) Classifier. For LogEn, accuracy of 100% for A-E, 99.34% for AB-E, and 98.67% for AC-E is achieved. While ShanEn combinations give accuracy of 96.67% for AB-E and 95.5% for ABC-E. These results showcase that our methodology is suitable for overcoming the problem and can become an alternate option for clinical diagnosis.  

Statistical features analysis and discrimination of maize seeds utilizing machine vision approach

2021 ◽  
Vol 40 (1) ◽  
pp. 703-714
Author(s):  
Aqib Ali ◽  
Wali Khan Mashwani ◽  
Muhammad H. Tahir ◽  
Samir Brahim Belhaouari ◽  
Hussam Alrabaiah ◽  
...  
Keyword(s):  
Machine Vision ◽  
Digital Image ◽  
Texture Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Statistical Features ◽  
Maize Seed ◽  
Maize Seeds ◽  
Image Dataset ◽  
Gray Level Image

The purpose of this study is the statistical analysis and discrimination of maize seed using a machine vision (MV) approach. The foundation of the digital image dataset holds six maize seed varieties named as Kargal K-9803, Gujjar Khan, Desi White, Pioner 30Y87, Syngenta ST-6142, and Pioner 31R88. The digital image dataset acquired via a digital imaging laboratory. For preprocessing, we crop the image into a size of 600×600 pixels, and convert it into a gray level image format. After that, line and edge detection are performed by using a Prewitt filter, and five non-overlapping areas of interest (AOIs) size of (200×200), and (250×250) are drawn. A total of 56 statistical features, containing texture features, histogram features, and spectral features, is extracted from each AOI. The 11 optimized statistical features have been selected by deploying “Correlation-based Feature Selection” (CFS) with the Greedy algorithm. For the discrimination analysis, four MV classifiers named as “Support Vector Machine” (SVM), “Logistic” (Lg), “Bagging” (B), and “LogitBoost” (LB) have been deployed on optimized statistical features dataset. After analysis, the SVM classifier has shown a promising accuracy of 99.93% on AOIs size (250×250). The obtained accuracy by SVM classifier on six maize seed varieties, namely Kargal K-9803, Gujjar Khan, Desi White, Pioner 30Y87, Syngenta ST-6142, and Pioner 31R88, were 99.9%, 99.8%, 100%, 100%, 99.9%, and 99.8%, respectively.

Text Localisation for Roman Words from Shop Signage

2017 ◽  
Vol 14 (2) ◽  
pp. 49
Author(s):  
Nurbaity Sabri ◽  
Noor Hazira Yusof ◽  
Zaidah` Ibrahim ◽  
Zolidah Kasiran ◽  
Nur Nabilah Abu Mangshor
Keyword(s):  
Comparative Study ◽  
Kernel Function ◽  
Texture Features ◽  
Text Recognition ◽  
Primary Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Histogram Of Oriented Gradient ◽  
Speeded Up Robust Features ◽  
Better Than

Text localisation determines the location of the text in an image. This process is performed prior to text recognition. Localising text on shop signage is a challenging task since the images of the shop signage consist of complex background, and the text occurs in various font types, sizes, and colours. Two popular texture features that have been applied to localise text in scene images are a histogram of oriented gradient (HOG) and speeded up robust features (SURF). A comparative study is conducted in this paper to determine which is better with support vector machine (SVM) classifier. The performance of SVM is influenced by its kernel function and another comparative study is conducted to identify the best kernel function. The experiments have been conducted using primary data collected by the authors. Results indicate that HOG with quadratic kernel function localises text for shop signage better than SURF.

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