Hyperspectral imaging (HSI) is an imaging modality that holds strong potential for rapid cancer detection during image-guided surgery. But the data from HSI often needs to be processed appropriately in order to extract the maximum useful information that differentiates cancer from normal tissue. We proposed a framework for hyperspectral image processing and quantification, which includes a set of steps including image preprocessing, glare removal, feature extraction, and ultimately image classification. The framework has been tested on images from mice with head and neck cancer, using spectra from 450- to 900-nm wavelength. The image analysis computed Fourier coefficients, normalized reflectance, mean, and spectral derivatives for improved accuracy. The experimental results demonstrated the feasibility of the hyperspectral image processing and quantification framework for cancer detection during animal tumor surgery, in a challenging setting where sensitivity can be low due to a modest number of features present, but potential for fast image classification can be high. This HSI approach may have potential application in tumor margin assessment during image-guided surgery, where speed of assessment may be the dominant factor.
KEYWORDS: Tumors, Tissues, Reflectivity, Hyperspectral imaging, Cancer, Green fluorescent protein, Image classification, Tissue optics, RGB color model, In vivo imaging
Early detection of malignant lesions could improve both survival and quality of life of cancer patients. Hyperspectral imaging (HSI) has emerged as a powerful tool for noninvasive cancer detection and diagnosis, with the advantage of avoiding tissue biopsy and providing diagnostic signatures without the need of a contrast agent in real time. We developed a spectral-spatial classification method to distinguish cancer from normal tissue on hyperspectral images. We acquire hyperspectral reflectance images from 450 to 900 nm with a 2-nm increment from tumor-bearing mice. In our animal experiments, the HSI and classification method achieved a sensitivity of 93.7% and a specificity of 91.3%. The preliminary study demonstrated that HSI has the potential to be applied in vivo for noninvasive detection of tumors.
Hyperspectral imaging is a developing modality for cancer detection. The rich information associated with hyperspectral images allow for the examination between cancerous and healthy tissue. This study focuses on a new method that incorporates support vector machines into a minimum spanning forest algorithm for differentiating cancerous tissue from normal tissue. Spectral information was gathered to test the algorithm. Animal experiments were performed and hyperspectral images were acquired from tumor-bearing mice. In vivo imaging experimental results demonstrate the applicability of the proposed classification method for cancer tissue classification on hyperspectral images.
As an emerging technology, hyperspectral imaging (HSI) combines both the chemical specificity of spectroscopy and the spatial resolution of imaging, which may provide a non-invasive tool for cancer detection and diagnosis. Early detection of malignant lesions could improve both survival and quality of life of cancer patients. In this paper, we introduce a tensor-based computation and modeling framework for the analysis of hyperspectral images to detect head and neck cancer. The proposed classification method can distinguish between malignant tissue and healthy tissue with an average sensitivity of 96.97% and an average specificity of 91.42% in tumor-bearing mice. The hyperspectral imaging and classification technology has been demonstrated in animal models and can have many potential applications in cancer research and management.
KEYWORDS: Tumors, Image registration, Hyperspectral imaging, Cancer, Tissues, Principal component analysis, In vivo imaging, Head, Surgery, Green fluorescent protein
The determination of tumor margins during surgical resection remains a challenging task. A complete removal of
malignant tissue and conservation of healthy tissue is important for the preservation of organ function, patient
satisfaction, and quality of life. Visual inspection and palpation is not sufficient for discriminating between malignant
and normal tissue types. Hyperspectral imaging (HSI) technology has the potential to noninvasively delineate surgical
tumor margin and can be used as an intra-operative visual aid tool. Since histological images provide the ground truth of
cancer margins, it is necessary to warp the cancer regions in ex vivo histological images back to in vivo hyperspectral
images in order to validate the tumor margins detected by HSI and to optimize the imaging parameters. In this paper,
principal component analysis (PCA) is utilized to extract the principle component bands of the HSI images, which is
then used to register HSI images with the corresponding histological image. Affine registration is chosen to model the
global transformation. A B-spline free form deformation (FFD) method is used to model the local non-rigid deformation.
Registration experiment was performed on animal hyperspectral and histological images. Experimental results from
animals demonstrated the feasibility of the hyperspectral imaging method for cancer margin detection.
Photodynamictherapy (PDT) uses a drug called a photosensitizer that is excited by irradiation with a laser light of a
particular wavelength, which generates reactive singlet oxygen that damages the tumor cells. The photosensitizer and
light are inert; therefore, systemic toxicities are minimized in PDT. The synthesis of novel PDT drugs and the use of
nanosized carriers for photosensitizers may improve the efficiency of the therapy and the delivery of the drug. In this
study, we formulated two nanoparticles with and without a targeting ligand to encapsulate phthalocyanines 4 (Pc 4)
molecule and compared their biodistributions. Metastatic human head and neck cancer cells (M4e) were transplanted into nude mice. After 2-3 weeks, the mice were injected with Pc 4, Pc 4 encapsulated into surface coated iron oxide (IO-Pc 4), and IO-Pc 4 conjugated with a fibronectin-mimetic peptide (FMP-IO-Pc 4) which binds specifically to integrin β1. The mice were imaged using a multispectral camera. Using multispectral images, a library of spectral signatures was created and the signal per pixel of each tumor was calculated, in a grayscale representation of the unmixed signal of each drug. An enhanced biodistribution of nanoparticle encapsulated PDT drugs compared to non-formulated Pc 4 was observed. Furthermore, specific targeted nanoparticles encapsulated Pc 4 has a quicker delivery time and accumulation in tumor tissue than the non-targeted nanoparticles. The nanoparticle-encapsulated PDT drug can have a variety of potential applications in cancer imaging and treatment.
An automatic framework is proposed to segment right ventricle on ultrasound images. This method can
automatically segment both epicardial and endocardial boundaries from a continuous echocardiography series by
combining sparse matrix transform (SMT), a training model, and a localized region based level set. First, the sparse
matrix transform extracts main motion regions of myocardium as eigenimages by analyzing statistical information of
these images. Second, a training model of right ventricle is registered to the extracted eigenimages in order to
automatically detect the main location of the right ventricle and the corresponding transform relationship between the
training model and the SMT-extracted results in the series. Third, the training model is then adjusted as an adapted
initialization for the segmentation of each image in the series. Finally, based on the adapted initializations, a localized
region based level set algorithm is applied to segment both epicardial and endocardial boundaries of the right ventricle
from the whole series. Experimental results from real subject data validated the performance of the proposed framework
in segmenting right ventricle from echocardiography. The mean Dice scores for both epicardial and endocardial
boundaries are 89.1%±2.3% and 83.6±7.3%, respectively. The automatic segmentation method based on sparse matrix
transform and level set can provide a useful tool for quantitative cardiac imaging.
Hyperspectral imaging (HSI) is an emerging modality for various medical applications. Its spectroscopic data might be able to be used to noninvasively detect cancer. Quantitative analysis is often necessary in order to differentiate healthy from diseased tissue. We propose the use of an advanced image processing and classification method in order to analyze hyperspectral image data for prostate cancer detection. The spectral signatures were extracted and evaluated in both cancerous and normal tissue. Least squares support vector machines were developed and evaluated for classifying hyperspectral data in order to enhance the detection of cancer tissue. This method was used to detect prostate cancer in tumor-bearing mice and on pathology slides. Spatially resolved images were created to highlight the differences of the reflectance properties of cancer versus those of normal tissue. Preliminary results with 11 mice showed that the sensitivity and specificity of the hyperspectral image classification method are 92.8% to 2.0% and 96.9% to 1.3%, respectively. Therefore, this imaging method may be able to help physicians to dissect malignant regions with a safe margin and to evaluate the tumor bed after resection. This pilot study may lead to advances in the optical diagnosis of prostate cancer using HSI technology.
The proposed macroscopic optical histopathology includes a broad-band light source which is selected to illuminate the
tissue glass slide of suspicious pathology, and a hyperspectral camera that captures all wavelength bands from 450 to
950 nm. The system has been trained to classify each histologic slide based on predetermined pathology with light
having a wavelength within a predetermined range of wavelengths. This technology is able to capture both the spatial
and spectral data of tissue. Highly metastatic human head and neck cancer cells were transplanted to nude mice. After 2-
3 weeks, the mice were euthanized and the lymph nodes and lung tissues were sent to pathology. The metastatic cancer
is studied in lymph nodes and lungs. The pathological slides were imaged using the hyperspectral camera. The results of
the proposed method were compared to the pathologic report. Using hyperspectral images, a library of spectral
signatures for different tissues was created. The high-dimensional data were classified using a support vector machine
(SVM). The spectra are extracted in cancerous and non-cancerous tissues in lymph nodes and lung tissues. The spectral
dimension is used as the input of SVM. Twelve glasses are employed for training and evaluation. The leave-one-out
cross-validation method is used in the study. After training, the proposed SVM method can detect the metastatic cancer
in lung histologic slides with the specificity of 97.7% and the sensitivity of 92.6%, and in lymph node slides with the
specificity of 98.3% and the sensitivity of 96.2%. This method may be able to help pathologists to evaluate many
histologic slides in a short time.
The current definitive diagnosis of prostate cancer is transrectal ultrasound (TRUS) guided biopsy. However, the current
procedure is limited by using 2D biopsy tools to target 3D biopsy locations. This paper presents a new method for
automatic segmentation of the prostate in three-dimensional transrectal ultrasound images, by extracting texture features
and by statistically matching geometrical shape of the prostate. A set of Wavelet-based support vector machines (WSVMs)
are located and trained at different regions of the prostate surface. The WSVMs capture texture priors of
ultrasound images for classification of the prostate and non-prostate tissues in different zones around the prostate
boundary. In the segmentation procedure, these W-SVMs are trained in three sagittal, coronal, and transverse planes.
The pre-trained W-SVMs are employed to tentatively label each voxel around the surface of the model as a prostate or
non-prostate voxel by the texture matching. The labeled voxels in three planes after post-processing is overlaid on a
prostate probability model. The probability prostate model is created using 10 segmented prostate data. Consequently,
each voxel has four labels: sagittal, coronal, and transverse planes and one probability label. By defining a weight
function for each labeling in each region, each voxel is labeled as a prostate or non-prostate voxel. Experimental results
by using real patient data show the good performance of the proposed model in segmenting the prostate from ultrasound images.
We present a 3D non-rigid registration algorithm for the potential use in combining PET/CT and transrectal ultrasound
(TRUS) images for targeted prostate biopsy. Our registration is a hybrid approach that simultaneously optimizes the
similarities from point-based registration and volume matching methods. The 3D registration is obtained by minimizing
the distances of corresponding points at the surface and within the prostate and by maximizing the overlap ratio of the
bladder neck on both images. The hybrid approach not only capture deformation at the prostate surface and internal
landmarks but also the deformation at the bladder neck regions. The registration uses a soft assignment and deterministic
annealing process. The correspondences are iteratively established in a fuzzy-to-deterministic approach. B-splines are
used to generate a smooth non-rigid spatial transformation. In this study, we tested our registration with pre- and postbiopsy
TRUS images of the same patients. Registration accuracy is evaluated using manual defined anatomic landmarks,
i.e. calcification. The root-mean-squared (RMS) of the difference image between the reference and floating images was
decreased by 62.6±9.1% after registration. The mean target registration error (TRE) was 0.88±0.16 mm, i.e. less than 3
voxels with a voxel size of 0.38×0.38×0.38 mm3 for all five patients. The experimental results demonstrate the
robustness and accuracy of the 3D non-rigid registration algorithm.
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