SignificanceDiagnosis of cancerous and pre-cancerous oral lesions at early stages is critical for the improvement of patient care, to increase survival rates and minimize the invasiveness of tumor resection surgery. Unfortunately, oral precancerous and early-stage cancerous lesions are often difficult to distinguish from oral benign lesions with the existing diagnostic tools used during standard clinical oral examination. In consequence, early diagnosis of oral cancer can be achieved in only about 30% of patients. Therefore, clinical diagnostic technologies for fast, minimally invasive, and accurate oral cancer screening are urgently needed.AimThis study investigated the use of multispectral autofluorescence imaging endoscopy for the automated and noninvasive discrimination of cancerous and precancerous from benign oral epithelial lesions.ApproachIn vivo multispectral autofluorescence endoscopic images of clinically suspicious oral lesions were acquired from 67 patients undergoing tissue biopsy examination. The imaged lesions were classified as precancerous (n=4), cancerous (n=29), and benign (n=34) lesions based on histopathology diagnosis. Multispectral autofluorescence intensity feature maps were generated for each oral lesion and used to train and optimize support vector machine (SVM) models for automated discrimination of cancerous and precancerous from benign oral lesions.ResultsAfter a leave-one-patient-out cross-validation strategy, an optimized SVM model developed with four multispectral autofluorescence features yielded levels of sensitivity and specificity of 85% and 71%, respectively and overall accuracy of 78% in the discrimination of cancerous/precancerous versus benign oral lesions.ConclusionThis study demonstrates the potentials of a computer-assisted detection system based on multispectral autofluorescence imaging endoscopy for the early detection of cancerous and precancerous oral lesions.
Multispectral autofluorescence endoscopy is a non-invasive optical imaging modality that can provide contrast between malignant and benign oral tissue. We hypothesized that discrimination of cancerous and precancerous from benign oral lesions can be achieved through machine-learning (ML) models developed with multispectral autofluorescence intensity features. In vivo multispectral autofluorescence endoscopic images of benign, precancerous, and cancerous oral lesions were acquired from 67 patients and used to optimize ML models for discrimination between cancerous/precancerous and benign lesions. This study demonstrates the potentials of a ML-assisted system based on multispectral autofluorescence endoscopy for automated discrimination of cancerous and precancerous from benign oral lesions.
Multispectral autofluorescence lifetime imaging (maFLIM) endoscopy can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral precancer and cancer. We tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used as features in machine-learning models to automatically discriminate precancerous and cancerous from healthy oral tissue. Clinical widefield maFLIM endoscopy images of cancerous and precancerous oral lesions from 57 patients were acquired and used to develop and validate a computer-aided detection (CAD) system. This study demonstrates the potentials of a maFLIM endoscopy-based CAD system for automated in situ clinical detection of oral precancer and cancer.
Compressive Sensing Magnetic Resonance Imaging (CS-MRI) has been rapidly developed during last several years.
To reconstruct an image from incomplete data using compressive sensing, the image has to be sparse or can be
transformed to sparse representation. Gradient operators associated with total variation (TV) and discrete wavelet
transform (DWT) are two commonly used sparsifying transforms in CS-MRI. Since the data acquired in MRI are
complex, these transforms are usually applied to the real and the imaginary parts of the image independently. In this
paper, we will explore the application of the complex wavelet transform (CWT) as a more effective sparsifying
transform for CS-MRI. Specifically, dual-tree complex wavelet transform (DT-CWT), a CWT previously used for
real or complex image compression, is integrated with compressive sensing reconstruction algorithm. We will test
the new method using both simulated and in-vivo MRI data. The results will be compared with those of DWT and
TV, which show that the new method can achieve better sparsity and reduced reconstruction errors in CS-MRI.
Spectral variability remains a challenging problem for target detection in hyperspectral (HS) imagery. In this paper, we
have applied the kernel-based support vector data description (SVDD) to perform full-pixel target detection. In target
detection scenarios, we do not have a collection of samples characterizing the target class; we are typically given a pure
target signature that is obtained from a spectral library. In our work, we use the pure target signature and first-order
Markov theory to generate N samples to model the spectral variability of the target class. We vary the value of N and
observe its effect to determine a value of N that provides acceptable detection performance.
We have inserted target signatures into an urban HS scene with varying levels of spectral variability to explore the
performance of the proposed SVDD target detection scheme in these scenarios. The proposed approach makes no
assumptions regarding the underlying distribution of the scene data as do traditional stochastic detectors such as the
adaptive matched filter (AMF). Detection results in the form of confusion matrices and receiver-operating-characteristic
(ROC) curves demonstrate that the proposed SVDD-based scheme is highly accurate and yields higher true positive rates
(TPR) and lower false positive rates (FPR) than the AMF.
Spectral variability remains a challenging problem for target detection in hyperspectral (HS) imagery. In previous work,
we developed a target detection scheme using the kernel-based support vector data description (SVDD). We constructed
a first-order Markov-based Gaussian model to generate samples to describe the spectral variability of the target class.
However, the Gaussian-generated samples also require selection of the variance parameter σ 2 that dictates the level of
variability in the generated target class signatures. In this work, we have investigated the use of decision-level fusion
techniques for alleviating the problem of choosing a proper value of σ 2 . We have trained a collection of SVDDs with
unique variance parameters σ 2 for each of the target training sets and have investigated their combination using the
traditional AND, OR, and majority vote (MV) decision-level rules. We have inserted target signatures into an urban HS
scene with differing levels of spectral variability to explore the performance of the proposed scheme in these scenarios.
Experiments show that the MV fusion rule is the best choice, providing relatively low false positive rates (FPR) while
yielding high true positive rates (TPR). Detection results show that the proposed SVDD-based decision-level scheme
using the MV fusion rule is highly accurate and yields higher true positive rates (TPR) and lower false positive rates
(FPR) than the adaptive matched filter (AMF).
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