Vira Oleksyuk

Non-invasive mechanical properties estimation of embedded objects using tactile imaging sensor

Non-invasive mechanical property estimation of an embedded object (tumor) can be used in medicine for characterization between malignant and benign lesions. We developed a tactile imaging sensor which is capable of detecting mechanical properties of inclusions. Studies show that stiffness of tumor is a key physiological discerning parameter for malignancy. As our sensor compresses the tumor from the surface, the sensing probe deforms, and the light scatters. This forms the tactile image. Using the features of the image, we can estimate the mechanical properties such as size, depth, and elasticity of the embedded object. To test the performance of the method, a phantom study was performed. Silicone rubber balls were used as embedded objects inside the tissue mimicking substrate made of Polydimethylsiloxane. The average relative errors for size, depth, and elasticity were found to be 67.5%, 48.2%, and 69.1%, respectively. To test the feasibility of the sensor in estimating the elasticity of tumor, a pilot clinical study was performed on twenty breast cancer patients. The estimated elasticity was correlated with the biopsy results. Preliminary results show that the sensitivity of 67% and the specificity of 91.7% for elasticity. Results from the clinical study suggest that the tactile imaging sensor may be used as a tumor malignancy characterization tool.

Characterization of Mammary Tumors Using Noninvasive Tactile and Hyperspectral Sensors

The use of both tactile and hyperspectral imaging sensors, which exploit the mechanical and physiological changes in tissues, can significantly increase the performance in automatic identification of tumors with malignant histopathology. Tactile imaging measures the elastic modulus of a tumor, whereas hyperspectral imaging detects important biochemical markers. Spontaneous mammary tumors in canines were used to demonstrate the efficacy of our approach. The tactile sensor achieved a sensitivity of 50% and a specificity of 100% in identifying malignant tumors. The sensitivity and specificity of the hyperspectral sensor were 71% and 76%, respectively. We investigated several machine learning techniques for fusing the tactile and spectral data, which increased the sensitivity and specificity to 86% and 97%, respectively. Our tactile and hyperspectral imaging sensors are noninvasive and harmless (no ionized radiation is used). These imaging sensors may not only eliminate unnecessary surgeries, but will also motivate the development of similar sensors for human clinical use, due to the fact that canine and human tumors have similar physiology and biology.

Tactile Imaging System for inclusion size and stiffness characterization

We developed Tactile Imaging System (TIS), which measures mechanical properties of tissue inclusions. The functional components of TIS include: a monochrome camera, a soft and transparent silicone probe, and an LED illumination circuit. The deformation of the soft TIS probe is used to evaluate size and compliance of inclusions, which is the inverse of elastic modulus. TIS algorithm development and testing were performed using a fabricated silicone tissue phantom with soft inclusions. We completed validation experiments for size and compliance estimation with TIS using the phantom. The results from these experiments suggest that TIS can be used for characterization of tissue inclusions.

Classification of breast masses using Tactile Imaging System and machine learning algorithms

In this study, we used Tactile Imaging System (TIS) and machine learning algorithms to classify breast masses in vivo as malignant or benign. When the silicone probe at the front end of TIS is compressed against the breast mass, the indentation profile of this waveguide is captured by a CCD camera. Then TIS algorithm determines the size and stiffness of inclusions based on the acquired tactile images. The size and stiffness results are then used as the input features for breast tumor classification algorithms. We compared three tumor classification algorithms: k-nearest neighbor, support vector machine, and Naïve Bayes, which are known to work well for limited data set. We tested these algorithms on twelve human breast tumors. The results were evaluated using the leave-one-out cross validation technique. Among the three algorithms, k-nearest neighbor classifier performed the best with sensitivity of 86% and specificity of 100%.

Tactile and hyperspectral imaging sensors for mammary tumor characterization

In this paper, we developed and tested both a tactile as well as a hyperspectral imaging sensor, which exploit the physiological and mechanical changes that occur in malignant tumors. The use of both modalities (tactile and hyperspectral) increases the accuracy of identifying malignant tumors. Spontaneous mammary tumors in dogs were used to test our sensors. The sensitivity and specificity of the fused tactile and hyperspectral data is 67% and 83%, respectively. These imaging sensors will not only decrease the need for unnecessary surgery, but it will also facilitate and jump-start the development of a tactile and hyperspectral imaging sensor for human clinical use because of the similarities between human and canine breast cancer.

Itchy skin region detection using hyperspectral imaging

Itch is the primary symptom of inflammatory skin diseases such as atopic eczema and psoriasis, chronic renal failure, and chronic hepatic failure. Itch, like pain, is a subjective symptom. Characterizing itchy skin and skin prone to itch will lead to better understanding of these symptoms and ultimately better diagnosis and treatment of the underlining disease. The goal of our study is to determine whether the itchy skin region can be detected by hyperspectral imaging. We used an imaging system equipped with liquid crystal tunable filter for collecting hyperspectral images. A halogen lamp was used to illuminate the region of interest. Images were taken from 650 nm to 1100 nm wavelength with 10 nm interval. The hyperspectral images were collected from the forearms of two male and two female subjects. An approximate 50 mm × 50 mm region of interest was marked on the forearms before imaging. The itch was mechanically induced. Imaging was performed for three conditions with a 99% Spectralon white diffuse reflectance target on the side: before inducing itch (normal region), after inducing itch (test region), and after removing itch (control region). Two methods were used to detect the itchy and nonitchy regions from the normalized hyperspectral data. The first method used a spectral distribution exploration method. The second method used a supervised classification method, more specifically, a support vector machine (SVM) algorithm. The spectral distribution exploration method did not detect any different spectral signature for itchy region. On the other hand, the SVM classifier detected the itchy region with the surrounding non-itchy region. These results demonstrated the feasibility of using hyperspectral imaging combined with classification algorithms for detecting itchy skin region.

Live demonstration: Tactile imaging sensor for mechanical properties quantification of breast tumor

In this live demonstration, a Tactile Imaging Sensor is presented, which is used for quantifying the mechanical properties of breast tumor. A tumor or malignancy feels different from the tissue surrounding it, which motivates the development of this system. Currently, a subjective method of clinical breast examination is performed for early detection of breast cancer. The method only provides qualitative information about the size and elastic modulus of suspicious lesion. To complement this method, we introduce a tactile imaging sensor that provides quantitative interpretations of the mechanical properties of embedded inclusion. In this sensor, a flexible Polydimethylsiloxane (PDMS) optical waveguide is used as the sensing probe. The sensing probe is illuminated by light emitting diodes in such a way that the total internal reflection occurs. When the embedded inclusion is compressed from the surface by the probe, the internally reflected light inside the probe is scattered. This scattered light is then captured by a near-infra-red CMOS camera, and forms the tactile images. Simultaneously, a load cell records the force reading. These images and force readings are then analyzed for estimating the size and elastic modulus of the embedded inclusion. This high resolution optical sensor offers a non-invasive and non-ionized technique for tumor screening. It is also a cost-effective and portable system, which requires minimal training of the operator. The demo will highlight the sensor use by estimating the mechanical properties of embedded inclusions inside a silicone phantom.

Cyber-physical tactile imaging system for malignant tumor identification

In this paper, we introduce a Cyber-Physical Tactile Imaging System (TIS) for identifying malignant tumors. The sensing is based on the principle of total internal reflection (TIR) of light. CP-TIS will estimate mechanical properties such as size and elasticity of the embedded objects (e.g. tumor). Based on the mechanical properties determined by TIS, it is possible to generate a malignancy score, since studies have shown that malignant tumors tend to be larger and stiffer. Clinical Breast Examination (CBE) is one of the qualitative techniques used by doctors for early detection of breast tumors. However, this method is effective only in detecting lesions, but not in classifying tumor malignancy. Also this is a subjective method where the performance depends on how the doctors perform CBE. Therefore, it is advantageous to quantify the mechanical properties of a tumor for objective tumor identification.

Demo abstract: Cyber-physical tactile imaging system for malignant tumor identification

In this paper, we introduce a Cyber-Physical Tactile Imaging System (TIS) for identifying malignant tumors. The sensing is based on the principle of total internal reflection (TIR) of light. CP-TIS will estimate mechanical properties such as size and elasticity of the embedded objects (e.g. tumor). Based on the mechanical properties determined by TIS, it is possible to generate a malignancy score, since studies have shown that malignant tumors tend to be larger and stiffer. Clinical Breast Examination (CBE) is one of the qualitative techniques used by doctors for early detection of breast tumors. However, this method is effective only in detecting lesions, but not in classifying tumor malignancy. Also this is a subjective method where the performance depends on how the doctors perform CBE. Therefore, it is advantageous to quantify the mechanical properties of a tumor for objective tumor identification.