Feature weighting for cancer tumor detection in mammography images using gravitational search algorithm

Feature weighting for cancer tumor detection in mammography images using gravitational search algorithm

Feature weighting for cancer tumor detection in mammography images using gravitational search algorithm

Abstract:

Optimization methods have been widely used in image processing and computer vision. In this paper, k-nearest neighbor (KNN) and real-valued gravitational search algorithm (RGSA) are used to detect the breast cancer tumors in mammography images. GSA is used as a tool for optimization of the features weighting (FW) and tuning the classifier. FW-KNN based on GSA is employed to enhance the K-NN classification accuracy. The weighted features and the tuned K-NN classifier are utilized for detecting tumors. The obtained results show good efficiency of GSA-based FW-KNN classification for breast cancer tumor detection.
Date of Conference: 20-20 Oct. 2016
Date Added to IEEE Xplore: 02 January 2017
ISBN Information:
INSPEC Accession Number: 16563552
Publisher: IEEE
Conference Location: Mashhad, Iran

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A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging

A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging

A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging

Abstract:

Goal: The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model. Methods: An automated algorithm based on a minimum spanning forest (MSF) and optimal band selection has been proposed to classify healthy and cancerous tissue on hyperspectral images. A support vector machine classifier is trained to create a pixel-wise classification probability map of cancerous and healthy tissue. This map is then used to identify markers that are used to compute mutual information for a range of bands in the hyperspectral image and thus select the optimal bands. An MSF is finally grown to segment the image using spatial and spectral information. Conclusion: The MSF based method with automatically selected bands proved to be accurate in determining the tumor boundary on hyperspectral images. Significance: Hyperspectral imaging combined with the proposed classification technique has the potential to provide a noninvasive tool for cancer detection.
Published in: IEEE Transactions on Biomedical Engineering ( Volume: 63, Issue: 3, March 2016 )
Date of Publication: 14 August 2015
ISSN Information:
PubMed ID: 26285052
INSPEC Accession Number: 15802414
Publisher: IEEE

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Rapid, Label-Free, and Highly Sensitive Detection of Cervical Cancer With Fluorescence Lifetime Imaging Microscopy

Rapid, Label-Free, and Highly Sensitive Detection of Cervical Cancer With Fluorescence Lifetime Imaging Microscopy

Rapid, Label-Free, and Highly Sensitive Detection of Cervical Cancer With Fluorescence Lifetime Imaging Microscopy

Abstract:

Cervical cancer is the second most common cancer in women worldwide and early detection of cervical cancer is crucial to improve the performance of treatment. Autofluorescence arising from cells and tissues can provide information of cellular energy metabolism. Fluorescence lifetime imaging microscopy (FLIM) can be used to detect the metabolism change indicating the development of precancer. In this study, cervical unstained tissue sections obtained from patients were detected by FLIM, which exhibited the cellular morphology features as clear as the pathology images without using fluorescence probes, and the average lifetime of normal tissue samples was consistently lower than that of precancerous or cancerous samples. The results indicate that FLIM is a rapid and label-free tool with high sensitivity and specificity to detect cervical cancer and precancer.
Published in: IEEE Journal of Selected Topics in Quantum Electronics ( Volume: 22, Issue: 3, May-June 2016 )
Article Sequence Number: 6801307
Date of Publication: 05 November 2015
ISSN Information:
INSPEC Accession Number: 15795269
Publisher: IEEE

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Infrared Fluorescence-Based Cancer Screening Capsule for the Small Intestine

Infrared Fluorescence-Based Cancer Screening Capsule for the Small Intestine

Infrared Fluorescence-Based Cancer Screening Capsule for the Small Intestine

Abstract:

Infrared fluorescence endoscopy (IRFE), in conjunction with an infrared fluorescent-labelling contrast agent, is a well known technique used for efficient early-stage cancer detection. In this paper we present a cost-effective ( <;$500) screening capsule prototype, which is able to detect infrared (IR) fluorescence emitted by indocyanine green (ICG) fluorophore dye. Rather than image, the capsule works as a high-sensitivity fluorometer that records fluorescence levels throughout the small intestine. The presented mixed-signal system has a small size, consumes very little power ( ≈6.3 mA) and does not require an external belt and hardware for data collection. By determining fluorescence levels in the intestine, rather than collecting images, we avoid the need for labour intensive video analysis. The whole system is contained within a compact ingestible capsule, that is sized so as to come into close contact with the intestine walls during peristalsis. Ex-vivo experiments, on ICG-impregnated swine intestine, have shown that the prototype system is able to detect low concentrations of ICG in the nanomolar and micromolar region, which is required to detect early cancer in the small intestine.
Published in: IEEE Transactions on Biomedical Circuits and Systems ( Volume: 10, Issue: 2, April 2016 )
Date of Publication: 21 August 2015
ISSN Information:
PubMed ID: 26302520
INSPEC Accession Number: 15823101
Publisher: IEEE

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Transurethral Photoacoustic Endoscopy for Prostate Cancer: A Simulation Study

Transurethral Photoacoustic Endoscopy for Prostate Cancer: A Simulation Study

Transurethral Photoacoustic Endoscopy for Prostate Cancer: A Simulation Study

Abstract:

The purpose of this study was to optimize the configuration of a photoacoustic endoscope (PAE) for prostate cancer detection and therapy monitoring. The placement of optical fiber bundles and ultrasound detectors was chosen to maximize the photoacoustic imaging penetration depth. We performed both theoretical calculations and simulations of this optimized PAE configuration on a prostate-sized phantom containing tumor and various photosensitizer concentrations. The optimized configuration of PAE with transurethral light delivery simultaneously increases the imaging penetration depth and improves image quality. Thermal safety, investigated via COMSOL Multiphysics, shows that there is only a 4 mK temperature rise in the urethra during photoacoustic imaging, which will cause no thermal damage. One application of this PAE has been demonstrated for quasi-quantifying photosensitizer concentrations during photodynamic therapy. The sensitivity of the photoacoustic detection for TOOKAD was 0.18 ng/mg at a 763 nm laser wavelength. Results of this study will greatly enhance the potential of prostate PAE for in vivo monitoring of drug delivery and guidance of the laser-induced therapy for future clinical use.
Published in: IEEE Transactions on Medical Imaging ( Volume: 35, Issue: 7, July 2016 )
Page(s): 1780 – 1787
Date of Publication: 11 February 2016
ISSN Information:
PubMed ID: 26886974
INSPEC Accession Number: 16105690
Publisher: IEEE

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Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities

Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities

Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities

Abstract:

Smart cities are a future reality for municipalities around the world. Healthcare services play a vital role in the transformation of traditional cities into smart cities. In this paper, we present a ubiquitous and quality computer-aided blood analysis service for the detection and counting of white blood cells (WBCs) in blood samples. WBCs also called leukocytes or leucocytes are the cells of the immune system that are involved in protecting the body against both infectious disease and foreign invaders. Analysis of leukocytes provides valuable information to medical specialists, helping them in diagnosing different important hematic diseases, such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and can be time-consuming. A mobile-cloud-assisted detection and classification of leukocytes from blood smear images can enhance accuracy and speed up the detection of WBCs. In this paper, we propose a smartphone-based cloud-assisted resource aware framework for localization of WBCs within microscopic blood smear images using a trained multi-class ensemble classification mechanism in the cloud. In the proposed framework, nucleus is first segmented, followed by extraction of texture, statistical, and wavelet features. Finally, the detected WBCs are categorized into five classes: basophil, eosinophil, neutrophil, lymphocyte, and monocyte. Experimental results on numerous benchmark databases validate the effectiveness and efficiency of the proposed system in comparison to the other state-of-the-art schemes.
Mobile-Cloud Assisted Leukocytes Segmentation and Classification System as a Resource-Aware Healthcare Service in Smart Cities.

Note: Special Section: Advances of Multisensory Services and Technologies for Healthcare in Smart Cities

Published in: IEEE Access ( Volume: 5 )
Date of Publication: 13 December 2016
INSPEC Accession Number: 16772330
Publisher: IEEE
Sponsored by: IEEE

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Entropy of Ultrasound-Contrast-Agent Velocity Fields for Angiogenesis Imaging in Prostate Cancer

Entropy of Ultrasound-Contrast-Agent Velocity Fields for Angiogenesis Imaging in Prostate Cancer

Entropy of Ultrasound-Contrast-Agent Velocity Fields for Angiogenesis Imaging in Prostate Cancer

Abstract:

Prostate cancer care can benefit from accurate and cost-efficient imaging modalities that are able to reveal prognostic indicators for cancer. Angiogenesis is known to play a central role in the growth of tumors towards a metastatic or a lethal phenotype. With the aim of localizing angiogenic activity in a non-invasive manner, Dynamic Contrast Enhanced Ultrasound (DCE-US) has been widely used. Usually, the passage of ultrasound contrast agents thought the organ of interest is analyzed for the assessment of tissue perfusion. However, the heterogeneous nature of blood flow in angiogenic vasculature hampers the diagnostic effectiveness of perfusion parameters. In this regard, quantification of the heterogeneity of flow may provide a relevant additional feature for localizing angiogenesis. Statistics based on flow magnitude as well as its orientation can be exploited for this purpose. In this paper, we estimate the microbubble velocity fields from a standard bolus injection and provide a first statistical characterization by performing a spatial entropy analysis. By testing the method on 24 patients with biopsy-proven prostate cancer, we show that the proposed method can be applied effectively to clinically acquired DCE-US data. The method permits estimation of the in-plane flow vector fields and their local intricacy, and yields promising results (receiver-operating-characteristic curve area of 0.85) for the detection of prostate cancer.
Published in: IEEE Transactions on Medical Imaging ( Volume: 36, Issue: 3, March 2017 )
Date of Publication: 16 November 2016
ISSN Information:
INSPEC Accession Number: 16707582
Publisher: IEEE

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Contrast-Enhanced Ultrasound Angiogenesis Imaging by Mutual Information Analysis for Prostate Cancer Localization

Contrast-Enhanced Ultrasound Angiogenesis Imaging by Mutual Information Analysis for Prostate Cancer Localization

Contrast-Enhanced Ultrasound Angiogenesis Imaging by Mutual Information Analysis for Prostate Cancer Localization

Abstract:

Objective: The role of angiogenesis in cancer growth has stimulated research aimed at noninvasive cancer detection by blood perfusion imaging. Recently, contrast ultrasound dispersion imaging was proposed as an alternative method for angiogenesis imaging. After the intravenous injection of an ultrasound-contrast-agent bolus, dispersion can be indirectly estimated from the local similarity between neighboring time-intensity curves (TICs) measured by ultrasound imaging. Up until now, only linear similarity measures have been investigated. Motivated by the promising results of this approach in prostate cancer (PCa), we developed a novel dispersion estimation method based on mutual information, thus including nonlinear similarity, to further improve its ability to localize PCa. Methods: First, a simulation study was performed to establish the theoretical link between dispersion and mutual information. Next, the method’s ability to localize PCa was validated in vivo in 23 patients (58 datasets) referred for radical prostatectomy by comparison with histology. Results: A monotonic relationship between dispersion and mutual information was demonstrated. The in vivo study resulted in a receiver operating characteristic (ROC) curve area equal to 0.77, which was superior (p = 0.21-0.24) to that obtained by linear similarity measures (0.74-0.75) and (p <; 0.05) to that by conventional perfusion parameters (≤0.70). Conclusion: Mutual information between neighboring time-intensity curves can be used to indirectly estimate contrast dispersion and can lead to more accurate PCa localization. Significance: An improved PCa localization method can possibly lead to better grading and staging of tumors, and support focal-treatment guidance. Moreover, future employment of the method in other types of angiogenic cancer can be considered.
Published in: IEEE Transactions on Biomedical Engineering ( Volume: 64, Issue: 3, March 2017 )
Date of Publication: 20 May 2016
ISSN Information:
PubMed ID: 27244713
INSPEC Accession Number: 16675204
Publisher: IEEE

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Breast Cancer Detection Using PCPCET and ADEWNN: A Geometric Invariant Approach to Medical X-Ray Image Sensors

Breast Cancer Detection Using PCPCET and ADEWNN: A Geometric Invariant Approach to Medical X-Ray Image Sensors

Breast Cancer Detection Using PCPCET and ADEWNN: A Geometric Invariant Approach to Medical X-Ray Image Sensors

Abstract:

In the field of radiology, mammographic screened images (i.e. X-ray image sensing) are very challenging and difficult to interpret. The expert radiologist visually hunts the mammograms for any specific abnormality. However, human factor causes a low degree of precision that often results in biopsy and anxiety for the patient involved. This paper proposes a novel computer-aided detection (CAD) system to reduce the human factor involvement and to help the radiologist in automatic diagnosis of malignant/nonmalignant breast tissues by utilizing polar complex exponential transform (PCET) moments as texture descriptors. The input region of interest is extracted manually and subjected to further number of preprocessing stages. Both magnitude and phase of PCET moments are used for feature extraction of suspicious region. Moreover, a new classifier adaptive differential evolution wavelet neural network is introduced to improve the classification accuracy of the proposed CAD system. The proposed system is tested on the mammographic images from Mammographic Image Analysis Society database. The designed system attains a fair accuracy of 97.965% with 98.196% sensitivity and 97.194% specificity. The best area under the receiver operational characteristics curve for the proposed classifier is found to be 0.984 with confidence interval from 0.968 to 0.999 and ±0.0108 standard error.
Published in: IEEE Sensors Journal ( Volume: 16, Issue: 12, June15, 2016 )
Date of Publication: 24 February 2016
ISSN Information:
INSPEC Accession Number: 16002872
Publisher: IEEE
Sponsored by: IEEE Sensors Council

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Detection of breast cancer from histopathology image and classifying benign and malignant state using fuzzy logic

Detection of breast cancer from histopathology image and classifying benign and malignant state using fuzzy logic

Detection of breast cancer from histopathology image and classifying benign and malignant state using fuzzy logic

Abstract:

Breast cancer is one of the major public health problem for women throughout the world. It has two states, known as benign and malignant. Benign state is slow growing, rarely spread to other parts of body and have well-defined borders. On the other hand, Malignant state has tendency to grow faster and it is life threatening. So, classification of this two state is crucial for proper diagnosis of a breast cancer patient. In this paper, we have introduced a new pipeline for breast cancer cell detection and feature extraction using an open source image analysis software named CellProfiler. We proposed an algorithm based on fuzzy inference system for classification of the benign and malignant state. Comparison using well known performance parameters such as accuracy, sensitivity and specificity shows that our proposed approach performs better than the Artificial Neural Network (ANN) and Support Vector Machine (SVM) based classification. The sensitivity, specificity, and accuracy of the proposed method is 95.6%, 90.63%, and 94.26% respectively.
Date of Conference: 22-24 Sept. 2016
Date Added to IEEE Xplore: 09 March 2017
ISBN Information:
INSPEC Accession Number: 16726596
Publisher: IEEE
Conference Location: Dhaka, Bangladesh

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