13 research outputs found

    Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

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    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 that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area

    Results of each step of the proposed cancer detection algorithm applied to the five different patients.

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    <p>(A), (B), (C), (D) and (E) Segmentation maps generated using the HKM algorithm. (F), (G), (H), (I) and (J) MV classification maps. (K), (L), (M), (N) and (O) OMD maps that take into account only the major probability per class obtained from the MV algorithm. (P), (Q), (R), (S) and (T) TMD maps that take into account the first three major probabilities per class obtained from the MV algorithm.</p

    Brain cancer detection and delimitation algorithm overview diagram.

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    <p>(A) HS cube of in-vivo brain surface. (B) Pre-processing stage of the algorithm. (C) Database of labeling samples generation. (D) SVM model training process employing the labeled samples dataset. (E), (F) and (G) Algorithms that conform the spatial-spectral supervised classification stage. (H) and (I) Algorithms that generate the unsupervised segmentation map and the final HELICoiD TMD map, respectively.</p
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