Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing (original) (raw)
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PloS one, 2018
Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework th...
Sensors
Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumo...
A Novel Use of Hyperspectral Images for Human Brain Cancer Detection using in-Vivo Samples
Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies, 2016
Hyperspectral Imaging is an emerging technology for medical diagnosis issues due to the fact that it is a noncontact, non-ionizing and non-invasive sensing technique. The work presented in this paper tries to establish a novel way in the use of hyperspectral images to help neurosurgeons to accurately determine the tumour boundaries in the process of brain tumour resection, avoiding excessive extraction of healthy tissue and the accidental leaving of un-resected small tumour tissues. So as to do that, a hyperspectral database of in-vivo human brain samples has been created and a procedure to label the pixels diagnosed by the pathologists has been described. A total of 24646 samples from normal and tumour tissues from 13 different patients have been obtained. A pre-processing chain to homogenize the spectral signatures has been developed, obtaining 3 types of datasets (using different pre-processing chain) in order to determine which one provides the best classification results using a Random Forest classifier. The experimental results of this supervised classification algorithm to distinguish between normal and tumour tissues have achieved more than 99% of accuracy.
Unsupervised classification of hyperspectral images by using linear unmixing algorithm
2009 16th IEEE International Conference on Image Processing (ICIP), 2009
This letter presents unsupervised hyperspectralimage classification based on fuzzy-clustering algorithms that spatially exploit membership relations. Not only is the conventional fuzzy c-means approach used to demonstrate the advantage of using membership relations but also Gustafson-Kessel clustering, which uses an adaptive distance norm, is, for the first time, used for the segmentation of hyperspectral images. A novel approach to include spatial information in the segmentation process is achieved by making use of spatial relations of fuzzy-membership functions among neighbor pixels. Two-and three-dimensional Gaussian filtering of fuzzy-membership degrees is utilized for this purpose. A novel phase-correlation-based similarity measure is used to further enhance the performance of the proposed approach by taking spatial relations into account for pixels with similar spectral characteristics only. It is shown that the proposed approach provides superior clustering performance for hyperspectral images.
In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection
IEEE Access, 2019
The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral algorithms for medical applications is the lack of specific, publicly available, and hyperspectral medical data. The work described in this paper was developed within the framework of the European project HELICoiD (HypErspectraL Imaging Cancer Detection), which had as a main goal the application of hyperspectral imaging to the delineation of brain tumors in real-time during neurosurgical operations. In this paper, the methodology followed to generate the first hyperspectral database of in-vivo human brain tissues is presented. Data was acquired employing a customized hyperspectral acquisition system capable of capturing information in the Visual and Near InfraRed (VNIR) range from 400 to 1000 nm. Repeatability was assessed for the cases where two images of the same scene were captured consecutively. The analysis reveals that the system works more efficiently in the spectral range between 450 and 900 nm. A total of 36 hyperspectral images from 22 different patients were obtained. From these data, more than 300 000 spectral signatures were labeled employing a semi-automatic methodology based on the spectral angle mapper algorithm. Four different classes were defined: normal tissue, tumor tissue, blood vessel, and background elements. All the hyperspectral data has been made available in a public repository. INDEX TERMS Hyperspectral imaging, cancer detection, biomedical imaging, medical diagnostic imaging, image databases. The associate editor coordinating the review of this manuscript and approving it for publication was Bora Onat.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012
Over the last years, many feature extraction techniques have been integrated in processing chains intended for hyperspectral image classification. In the context of supervised classification, it has been shown that the good generalization capability of machine learning techniques such as the support vector machine (SVM) can still be enhanced by an adequate extraction of features prior to classification, thus mitigating the curse of dimensionality introduced by the Hughes effect. Recently, a new strategy for feature extraction prior to classification based on spectral unmixing concepts has been introduced. This strategy has shown success when the spatial resolution of the hyperspectral image is not enough to separate different spectral constituents at a sub-pixel level. Another advantage over statistical transformations such as principal component analysis (PCA) or the minimum noise fraction (MNF) is that unmixing-based features are physically meaningful since they can be interpreted as the abundance of spectral constituents. In turn, previously developed unmixing-based feature extraction chains do not include spatial information. In this paper, two new contributions are proposed. First, we develop a new unmixing-based feature extraction technique which integrates the spatial and the spectral information using a combination of unsupervised clustering and partial spectral unmixing. Second, we conduct a quantitative and comparative assessment of unmixing-based versus traditional (supervised and unsupervised) feature extraction techniques in the context of hyperspectral image classification. Our study, conducted using a variety of hyperspectral scenes collected by different instruments, provides practical observations regarding the utility and type of feature extraction techniques needed for different classification scenarios.
Spectral organ fingerprints for intraoperative tissue classification with hyperspectral imaging
2021
Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the method’s current lack of robustness and generalizability. Specifically, it had been unknown whether variability in spectral reflectance is primarily explained by tissue type rather than the recorded individual or specific acquisition conditions. The contribution of this work is threefold: (1) Based on an annotated medical HSI data set (9,059 images from 46 pigs), we present a tissue atlas featuring spectral fingerprints of 20 different porcine organs and tissue types. (2) Using the principle of mixed model analysis, we show that the greatest source of variability related to HSI images is the organ under observation. (3)...
An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation
Sensors (Basel, Switzerland), 2018
Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400-1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,0...
Hyperspectral Imaging: A comparative study of supervised learning algorithms
2023
This study focuses on the hyperspectral image from the University of Pavia, performing various manipulations to derive new datasets and observe their impact on the classification results. The aim is to automate the pixel classification process using machine learning algorithms with different training and testing splits. Additionally, ensemble classifiers were implemented to improve accuracy. The results show that the Multilayer Perceptron (MLP) achieved the highest accuracy among the implemented methods, surpassing 85% and providing similar results to the ensemble classifiers. The original dataset (untouched) and the dataset reduced to 20 principal components using Principal Component Analysis (PCA) yielded the best results. It is worth noting that considering unlabeled pixels limited the accuracy of the implemented algorithms.
Unmixing Prior to Supervised Classification of Remotely Sensed Hyperspectral Images
IEEE Geoscience and Remote Sensing Letters, 2000
Supervised classification of hyperspectral images is a very challenging task due to the generally unfavorable ratio between the number of spectral bands and the number of training samples available a priori, which results in the Hughes phenomenon. For this purpose, several feature extraction methods have been investigated in order to reduce the dimensionality of the data to the right subspace without significant loss of the original information that allows for the separation of classes. In this letter, we explore the use of spectral unmixing for feature extraction prior to supervised classification of hyperspectral data using support vector machines. The proposed feature extraction strategy has been implemented in the form of four different unmixing chains and evaluated using two different scenes collected by National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible/Infrared Imaging Spectrometer. The experiments suggest competitive results but also show that the definition of the unmixing chains plays an important role in the final classification accuracy. Moreover, differently from most feature extraction techniques available in the literature, the features obtained using linear spectral unmixing are potentially easier to interpret due to their physical meaning.