A Minimum Spanning Forest Based Hyperspectral Image Classification Method for Cancerous Tissue Detection.

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.

Targeted iron-oxide nanoparticle for photodynamic therapy and imaging of head and neck cancer.

Photodynamic therapy (PDT) is a highly specific anticancer treatment modality for various cancers, particularly for recurrent cancers that no longer respond to conventional anticancer therapies. PDT has been under development for decades, but light-associated toxicity limits its clinical applications. To reduce the toxicity of PDT, we recently developed a targeted nanoparticle (NP) platform that combines a second-generation PDT drug, Pc 4, with a cancer targeting ligand, and iron oxide (IO) NPs. Carboxyl functionalized IO NPs were first conjugated with a fibronectin-mimetic peptide (Fmp), which binds integrin ß1. Then the PDT drug Pc 4 was successfully encapsulated into the ligand-conjugated IO NPs to generate Fmp-IO-Pc 4. Our study indicated that both nontargeted IO-Pc 4 and targeted Fmp-IO-Pc 4 NPs accumulated in xenograft tumors with higher concentrations than nonformulated Pc 4. As expected, both IO-Pc 4 and Fmp-IO-Pc 4 reduced the size of HNSCC xenograft tumors more effectively than free Pc 4. Using a 10-fold lower dose of Pc 4 than that reported in the literature, the targeted Fmp-IO-Pc 4 NPs demonstrated significantly greater inhibition of tumor growth than nontargeted IO-Pc 4 NPs. These results suggest that the delivery of a PDT agent Pc 4 by IO NPs can enhance treatment efficacy and reduce PDT drug dose. The targeted IO-Pc 4 NPs have great potential to serve as both a magnetic resonance imaging (MRI) agent and PDT drug in the clinic.

Automatic Segmentation of Right Ventricle on Ultrasound Images Using Sparse Matrix Transform and Level Set.

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.

Biodistribution Study of Nanoparticle Encapsulated Photodynamic Therapy Drugs Using Multispectral Imaging.

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 ß1. 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.

Hyperspectral imaging and quantitative analysis for prostate cancer detection.

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.

Detection of Cancer Metastasis Using a Novel Macroscopic Hyperspectral Method.

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.

3D Segmentation of Prostate Ultrasound images Using Wavelet Transform.

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 (W-SVMs) 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.