Shape analysis of the human brain.

Nitzken,; Joseph, Matthew

doi:10.18297/etd/2067

Cited by 3 publications

(2 citation statements)

References 182 publications

(297 reference statements)

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“…In this coordinate system, the tumor’s surface may be considered a function of the polar and azimuthal angle,

, which can be expressed as a linear combination of basis functions

defined on the unit sphere. Starting with a discrete approximation of the surface, i.e., a triangular mesh, the proposed algorithm uses an attraction–repulsion technique [ 41 ] to map this mesh to the unit sphere. The mapping fixes the image of each mesh vertex at the unit distance from the origin, while preserving the mesh topology and maintaining the distance between adjacent vertices as much as possible.…”

Section: Methodsmentioning

confidence: 99%

A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors

Shehata

Alksas

Abouelkheir

et al. 2021

Sensors

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Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study (male = 72 and female = 68, age range = 15 to 87 years). There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of 95.3%±2.0%, a specificity of 99.9%±0.4%, and Dice similarity coefficient of 0.98±0.01 in differentiating malignant from benign tumors, as well as an overall accuracy of 89.6%±5.0% in discriminating ccRCC from nccRCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The obtained results using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, relational functional gradient boosting, etc.). Hence, integrating morphological, textural, and functional features enhances the diagnostic performance, making the proposal a reliable noninvasive diagnostic tool for renal tumors.

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“…In this coordinate system, the tumor’s surface may be considered a function of the polar and azimuthal angle,

, which can be expressed as a linear combination of basis functions

Section: Methodsmentioning

confidence: 99%

A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors

Shehata

Alksas

Abouelkheir

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In this coordinate system, the surface of the region can be deemed a function of the polar and azimuth angle, 𝑓 Ɵ, φ , which can be expressed as a linear set of base functions Yτβ specified in the unit sphere. The modeling of spherical harmonics constructs a triangulated mesh approximating the surface of the lesion, afterwards maps it to the unit sphere by employing the attraction-repulsion technique [44]. Each cycle (α) of the attraction-repulsion approach operates as follows.…”

Section: Shape Featuresmentioning

confidence: 99%

A New Framework for Precise Identification of Prostatic Adenocarcinoma

Ayyad

Badawy

Shehata

et al. 2022

Sensors

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Prostate cancer, which is also known as prostatic adenocarcinoma, is an unconstrained growth of epithelial cells in the prostate and has become one of the leading causes of cancer-related death worldwide. The survival of patients with prostate cancer relies on detection at an early, treatable stage. In this paper, we introduce a new comprehensive framework to precisely differentiate between malignant and benign prostate cancer. This framework proposes a noninvasive computer-aided diagnosis system that integrates two imaging modalities of MR (diffusion-weighted (DW) and T2-weighted (T2W)). For the first time, it utilizes the combination of functional features represented by apparent diffusion coefficient (ADC) maps estimated from DW-MRI for the whole prostate in combination with texture features with its first- and second-order representations, extracted from T2W-MRIs of the whole prostate, and shape features represented by spherical harmonics constructed for the lesion inside the prostate and integrated with PSA screening results. The dataset presented in the paper includes 80 biopsy confirmed patients, with a mean age of 65.7 years (43 benign prostatic hyperplasia, 37 prostatic carcinomas). Experiments were conducted using different well-known machine learning approaches including support vector machines (SVM), random forests (RF), decision trees (DT), and linear discriminant analysis (LDA) classification models to study the impact of different feature sets that lead to better identification of prostatic adenocarcinoma. Using a leave-one-out cross-validation approach, the diagnostic results obtained using the SVM classification model along with the combined feature set after applying feature selection (88.75% accuracy, 81.08% sensitivity, 95.35% specificity, and 0.8821 AUC) indicated that the system’s performance, after integrating and reducing different types of feature sets, obtained an enhanced diagnostic performance compared with each individual feature set and other machine learning classifiers. In addition, the developed diagnostic system provided consistent diagnostic performance using 10-fold and 5-fold cross-validation approaches, which confirms the reliability, generalization ability, and robustness of the developed system.

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