STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The presently-disclosed subject matter relates to a method and system for monitoring and evaluation of pressure ulcer (bedsore) severity.

2. Description of Related Art

Patients in hospitals and long-term care facilities suffer from pressure ulcers. The cause of such ulcers is the prolonged pressure of the soft tissue between a bone prominence and an outer surface which causes localized skin injuries. Decreased blood flow due to localized pressure causes an ischemic process that takes place under the skin, making it difficult to diagnose its appearance until the wound is in an advanced state and becomes visible [1] [2]. When detection and diagnosis is delayed, health cost and deterioration of health condition occurs usually due to infection [3]. As shown inFIG. 1, various parts of the body may suffer from pressure ulcers such as the skin overlying the sacrum, coccyx, heels or the hips, but other sites such as the elbows, knees, ankles or the back of the cranium can be affected [4]. Other commonly used names are pressure sores and bed sores [5]. Preventive measures require reduction of prolonged stay without movement to reduce compression and local occlusions of blood capillaries.

Why are pressure ulcers important? In the US, the number of affected is around 2.5 million patients per year and the cost around $9.1-$11.6 billion per year. Cost of individual patient care ranges from $20,900 to $151,700 per pressure ulcer. Medicare estimated in 2007 that each pressure ulcer added $43,180 in costs to a hospital stay. More than 17,000 lawsuits are related to pressure ulcers annually. It is the second most common claim after wrongful death and greater than falls or emotional distress. Related to death, about 60,000 patients die as a direct result of a pressure ulcer each year [7]. Figures in Europe show that about the 18% of patients staying in a public or private hospital develop bedsores. The elderly are 70% of the total of patients with wounds. First weeks in hospitals are especially important because about 50% of patients develop the injuries during this time [3]. Related the death probability, the 25% of patients die because of wounds. Speaking about cost, the average treatment of bedsores in Europe ranges between 1500€ and 17000€. Some examples: the cost in Spain is around 461€ millions [9] and in the UK around 1.8 million € per year [8]. Following the classification of the National Pressure Ulcer Advisory Panel (NPUAP) [6], wounds can be associated to four different stages. In stage I, there is no loss of skin jet but the skin shows redness around the area of pressure and the affected area differs due to changes in the temperature and also the skin thickness. On stage II the bedsore has the appearance of a burn with reddish pink color and suffers of partial loss of skin thickness. On stage III the bedsore has lost full skin thickness. The stage IV is the most advanced suffering of a full skin thickness loss with exposed muscles, tendons or even bones. The patient has a higher risks of infection and the existing treatments are very painful. These visual used nowadays are invasive, painful and not as precise as required. If the patient suffers of a pressure ulcer, it is essential to proceed to make an assessment by taking into account the PUSH (Pressure Ulcer Scale for Healing) scale, which allows evaluating the evolution of the wound [12].

The clinical need addressed is related the extreme discomfort to the patient and often lead to serious, life threatening infections, which substantially increase the total cost of care. Pressure ulcers typically result from prolonged periods of uninterrupted pressure on the skin, soft tissue, muscle, and bone. Vulnerable patients include the elderly, stroke victims, patients with diabetes, those with dementia, and people who use wheelchairs or who are bedridden—any patient with impaired mobility or sensation.

Currently, nurses and medical personnel do tracking of wound/ulcer progress in a subjective manner. They may use a printed sheet to enter measurements of the wound length and width as well as their perception of tissue distribution. Standardized paper forms exist for different facilities, but there is no exact automated measurement.

BRIEF SUMMARY OF THE INVENTION

Generally described, an exemplary method for evaluating a pressure ulcer according to the invention includes: a pre-processing step of detecting in a color input image a boundary of the pressure ulcer to get a RGB region of interest (ROI) image; converting the RGB ROI image to a grayscale ROI image; processing the grayscale ROI image using a Linear Combination of Discrete Gaussians (LCDG) process to estimate three main class probabilities of the grayscale ROI image; processing the RGB ROI image using predetermined RGB values to estimate the three main class probabilities of the RGB ROI image; combining the estimated three main class probabilities of the grayscale ROI image and the estimated three main class probabilities of the RGB ROI image to determining an estimated labeled image; and normalizing and refining the estimated labeled image using a Generalized Gauss-Markov Random Field (GGMRF) process to produce a final segmentation image. Advantageously, the final segmentation allows an objective evaluation of the pressure ulcer because it provides an accurate classification of the tissue in the ulcer. The classification can then be used to objectively determine relative percentages of each of the tissue types (i.e., the tissue distribution) for evaluating the severity of the pressure ulcer.

In accordance with one implementation, processing the grayscale ROI image using a LCDG process includes: collecting a marginal empirical probability distribution Fs=(fs(q): q∈Q) of gray levels; finding an initial LCDG-model that closely approximates Fs by using an initializing process to estimate numbers Cp−K, Cn, and parameters w, Θ (weights, means, and variances) of positive and negative Discrete Gaussians (DGs); refining the initial LCDG-model with the numbers Cpand Cnby adjusting all other parameters with an expectation-maximization (EM) method to create a final LCDG-model; splitting the final LCDG-model into K sub-models, one per each dominant mode, by minimizing expected errors of misclassification; and selecting from the K sub-models a sub-model with a largest mean value as a desired model. The steps of finding the initial LCDG-model that closely approximates Fs and refining the initial LCDG-model produce a LCDG-model with nonnegative starting probabilities pw,Θ(q). The steps of splitting the final LCDG-model into K sub-models and selecting from the K sub-models a sub-model with a largest mean value increase the likelihood that the probabilities continue to be nonnegative. Thus, at the end of these steps, three probabilities for each pixel in the grayscale ROI image are obtained: P1which is the probability to be belonging to class 1, P2which is the probability to be belonging to class 2, and P3which is the probability to be belonging to class 3.

In accordance with another implementation, processing the RGB ROI image using predetermined RGB values includes: constructing a database for a set of labeled images for three main tissue classes; constructing a matrix for each class by aggregating RGB values of all pixels of that class from training images which have been labeled; for each pixel in the RGB ROI image, calculating Euclidean distances between the pixel and the matrix for each class; determining for each of the three main tissue classes a total number of voxels that are at distances lower than a predefined threshold; and estimating the three main class probabilities for each pixel in the RGB ROI image using individual probabilities.

In accordance with yet another implementation, combining the estimated three main class probabilities of the grayscale ROI image and the estimated three main class probabilities of the RGB ROI image includes: constructing the final pixel-wise probability vector as P=PGray×PColor; and obtaining the estimated labeled image as an initial tissue classification of each pixel using a vector element with a highest probability.

Another aspect of the invention is a method of monitoring a pressure ulcer. The method includes determining an objective change in the pressure ulcer by comparing current results of the method of evaluating the pressure ulcer described above with prior results of the method. Advantageously, the objective determinations of tissue distribution allow objective monitoring of the severity of the pressure ulcer over time.

In yet another aspect of the invention, a system for monitoring and evaluating a pressure ulcer, includes: a microprocessor; a data storage device in communication with the microprocessor; an input/output device in communication with the microprocessor; and a digital camera in communication with the microprocessor. The microprocessor is for executing instructions stored in the data storage device for: receiving a color input image of the pressure ulcer from the digital camera; detecting in the color input image a boundary of the pressure ulcer to get a RGB region of interest (ROI) image; converting the RGB ROI image to a grayscale ROI image; processing the grayscale ROI image using a Linear Combination of Discrete Gaussians (LCDG) process to estimate three main class probabilities of the grayscale ROI image; processing the RGB ROI image using predetermined RGB values to estimate the three main class probabilities of the RGB ROI image; combining the estimated three main class probabilities of the grayscale ROI image and the estimated three main class probabilities of the RGB ROI image to determining an estimated labeled image; normalizing and refining the estimated labeled image using a Generalized Gauss-Markov Random Field (GGMRF) process to produce a final segmentation image; and outputting the final segmentation image to the input/output device. Advantageously, the instructions may be optimized for execution on a mobile device, such as a smart phone or a personal digital assistant (PDA) with respect to execution speed, memory requirements, and integration of the digital camera and power supply so that the device can be carried by a care giver and used at the patient's bedside.

In accordance with one implementation of the exemplary system, processing the grayscale ROI image using a LCDG process includes: collecting a marginal empirical probability distribution Fs=(fs(q): q∈Q) of gray levels; finding an initial LCDG-model that closely approximates Fs by using an initializing process to estimate numbers Cp−K, Cn, and parameters w, Θ (weights, means, and variances) of positive and negative Discrete Gaussians (DGs); refining the initial LCDG-model with the numbers Cpand Cnby adjusting all other parameters with an expectation-maximization (EM) method to create a final LCDG-model; splitting the final LCDG-model into K sub-models, one per each dominant mode, by minimizing expected errors of misclassification; and selecting from the K sub-models a sub-model with a largest mean value as a desired model. The steps of finding the initial LCDG-model that closely approximates Fs and refining the initial LCDG-model produce a LCDG-model with nonnegative starting probabilities pw,Θ(q). The steps of splitting the final LCDG-model into K sub-models and selecting from the K sub-models a sub-model with a largest mean value increase the likelihood that the probabilities continue to be nonnegative. Thus, at the end of these steps, three probabilities for each pixel in the grayscale ROI image are obtained: P1which is the probability to be belonging to class 1, P2which is the probability to be belonging to class 2, and P3which is the probability to be belonging to class 3.

In accordance with another implementation of the exemplary system, processing the RGB ROI image using predetermined RGB values includes: constructing a database for a set of manually labeled images for three main tissue classes; constructing a matrix for each class by aggregating RGB values of all pixels of that class from training images which have been manually labeled by an expert; for each pixel in the RGB ROI image, calculating Euclidean distances between the pixel and the matrix for each class; determining for each of the three main tissue classes a total number of voxels that are at distances lower than a predefined threshold; and estimating the three main class probabilities for each pixel in the RGB ROI image using individual probabilities.

In accordance with yet another implementation of the exemplary system, combining the estimated three main class probabilities of the grayscale ROI image and the estimated three main class probabilities of the RGB ROI image includes: constructing the final pixel-wise probability vector as P=PGray×PColor; and obtaining the estimated labeled image as an initial tissue classification of each pixel using a vector element with a highest probability.

In accordance with still yet another implementation, the exemplary system includes a power supply in communication with the microprocessor, the data storage device, the input/output device, and the digital camera. The integrated power supply, the microprocessor, the data storage device, the input/output device, and the digital camera are integrated into a unitary mobile device that can be carried by a caregiver and utilized at the bedside of a patient. Advantageously, the input/output device may be a touchscreen, and the unitary mobile device may be a commercially available smart phone or personal digital assistant (PDA).

