METHOD AND SYSTEM OF AUTOMATED DETECTION OF LESIONS IN MEDICAL IMAGES

• US 20100158332A1
• Filed: 12/21/2009
• Published: 06/24/2010
• Est. Priority Date: 12/22/2008
• Status: Abandoned Application

• Current Assignee: The Medipattern Corp.
• Original Assignee: The Medipattern Corp.
• Inventors: Rico, Dan, Chung, Desmond Ryan
• Application Number:
  US12/643,337
• Publication Number:
  US 20100158332A1
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: A61B 5/0033 Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room image data processing or generation G06T
• CPC Class Codes: A61B 5/4312 Breast evaluation or disorder diagnosis A61B5/0091 takes precedence
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 8/0825 for diagnosis of the breast, e.g. mammography
• CPC Class Codes: A61B 8/5223 for extracting a diagnostic or physiological parameter from medical diagnostic data for algorithms to analyse biomedical images G06T7/0012
• CPC Class Codes: A61B 8/5269 involving detection or reduction of artifacts for image enhancement algorithms G06T5/00
• CPC Class Codes: G06T 2207/10132 Ultrasound image
• CPC Class Codes: G06T 2207/20192 Edge enhancement; Edge preservation
• CPC Class Codes: G06T 2207/30096 Tumor; Lesion
• CPC Class Codes: G06T 5/92 based on global image properties
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

The invention provides a system and method for processing medical images. Input medical images are normalized first, utilizing pixel intensities of control point tissues, including subcutaneous fat. Clustered density map and malignance probability map are generated from a normalized image and further analyzed to identify regions of common internal characteristics, or blobs, that may represent lesions. These blobs are analyzed and classified to differentiate possible true lesions from other types of non-malignant masses often seen in medical images.

Claims

1. A method of identifying suspected lesions in an ultrasound medical image, comprising the steps of:

computing an estimated representative fat intensity value of subcutaneous fat pixels in the medical image,

calculating normalized grey pixel values from pixel values of the medical image utilizing a mapping relationship between a normalized fat intensity value and the representative fat intensity value to obtain a normalized image,

identifying pixels in the normalized image forming distinct areas, each of the distinct areas having consistent internal characteristics,

extracting descriptive features from each of the distinct areas,

analyzing the extracted descriptive features of the each distinct area and assigning to the each distinct area a likelihood value of the each distinct area being a lesion, and

identifying all distinct areas having likelihood values satisfying a pre-determined criteria as candidate lesions.

2. The method of claim 1, further comprising the step of de-noising the medical image to obtain a de-noised image prior to calculating normalized grey pixel values of the medical image and wherein the normalized grey pixel values are calculated from pixel values of the de-noised image.

3. The method of claim 2, wherein the step of de-noising further comprises applying an edge-preserving diffusion image filtering to the medical image.

4. The method of claim 2, wherein the step of de-noising further comprises applying to the medical image a selective filtering that preserves sharpness of edges and encourages intra-region smoothing.

5. The method of claim 1, further comprising:

selecting a subcutaneous fat region in the ultrasound image,

wherein the estimated representative fat intensity value is computed from pixels in the selected subcutaneous fat region.

6. The method of claim 5, wherein the step of computing the estimated representative fat intensity value further comprises applying k-means clustering to the selected subcutaneous fat region.

7. The method of claim 6, wherein the step of computing the estimated representative fat intensity value further comprises computing a mean value of pixel intensities of pixels in a mid-level intensity cluster obtained from the k-means clustering.

8. The method of claim 1, further comprising:

selecting a plurality of control point tissues, the plurality of control point tissues including subcutaneous fat,

clustering intensities of pixels to generate intensity clusters, each one of the plurality of control point tissues being represented by at least one of the intensity clusters, and

for each of the plurality of control point tissues,

determining a representative intensity value for the each control point tissue from grey intensity values of pixels of the corresponding at least one intensity cluster,

assigning a normalized intensity value to the each control point tissue,

wherein the mapping relationship relates the normalized intensity values to their respective representative intensity values of the plurality of the control point tissues.

9. The method of claim 1, further comprising:

estimating a malignancy probability value for each pixel of the normalized image to generate a malignancy probability map,

wherein the step of identifying distinct areas includes;

grouping pixels with malignancy probability values above a threshold value in contiguous regions to form the distinct areas.

10. The method of claim 9, wherein the step of estimating the malignancy probability value further comprises:

establishing a logistic model calibrated on a collection of sample images, the logistic model incorporating image pixel values and hardware and operator acquisition parameters, and

applying the logistic model to the normalized image to estimate the malignancy probability value.

11. A system for automatically identifying regions in a medical image that likely correspond to lesions, the system comprising:

an intensity unit, the intensity unit being configured to compute estimated intensities of control point tissues in the medical image from pixel values in the medical image and

a normalization module, the normalization unit being configured to generate a mapping relationship between an input pixel and a normalized pixel and convert a grey pixel value to a normalized pixel value to obtain a normalized image according to the mapping relationship;

a map generation module, the map generation module assigning a parameter value to each pixel in an input image to generate a parameter map;

a blob detection module, the blob detection module being configured to detect and demarcate blobs in the parameter map;

a feature extraction unit, the feature extraction unit being configured to detect and compute descriptive features of the detected blobs; and

a blob analysis module, the blob analysis module computing from descriptive features of a blob an estimated likelihood value that the blob is malignant and assigning the likelihood value to the blob.

12. The system of claim 11, wherein the control point tissues include at least one of subcutaneous fat, anechoic cyst, skin, and fibroglandular tissues.

13. The system of claim 11, wherein the map generation module includes a density map module, the parameter value is density and the parameter map is a density map.

14. The system of claim 11, wherein the map generation module further includes a malignance probability map module, the malignance probability module computing a malignancy probability for each pixel and assigning the malignant probability to the each pixel to generate a malignance probability map, and blobs detected and demarcated by the detection module in the malignance probability map being included in the blobs processed by the feature extraction unit and the blob analysis module.

15. The system of claim 11, further comprising a pre-processing module, the pre-processing module de-noising the medical image to generate a de-noised image.

16. The system of claim 11, further comprising a reporting module, the reporting module identifying all blobs having likelihood values above a pre-determined threshold value.

17. A method of estimating grey scale intensity of a tissue in a digitized medical image, the method comprising the steps of:

applying a clustering operation to intensity values of pixels of the medical image to group the intensity values into distinct intensity clusters,

identifying one of the distinct intensity clusters as an intensity cluster corresponding to the tissue according to relative strength of the tissue in relation to other tissues imaged in the digitized medical image,

estimating a representative grey scale intensity value of the intensity cluster from grey scale intensities of pixels of the intensity cluster; and

assigning the representative grey scale intensity to the tissue.

18. The method of claim 17, wherein the representative grey scale intensity is a mean intensity of the grey scale intensities.

19. The method of claim 17, wherein the tissue is subcutaneous fat.

20. The method of claim 17, wherein the clustering algorithm is a k-means algorithm having k=3 and the identified distinct cluster is a mid-intensity cluster.

21. The method of claim 17, further comprising:

demarcating a region in the medical image where the tissue is expected to lie, wherein the clustering operation is applied to the demarcated region.

22. A method of processing an ultrasound breast image, the method comprising the steps of:

constructing a layered model of breast, each pair of neighboring layers of the model defining a boundary surface between the each pair of neighboring layers,

calibrating the model on a plurality of sample ultrasound breast images, each of the plurality of sample ultrasound breast images being manually segmented to identify the boundary surfaces in the sample ultrasound breast images, the calibrated model comprising parameterized surface models, each parameterized surface model comprising a set of boundary surface look-up tables (LUTs) corresponding to a discrete value of a size parameter,

receiving an estimated value of the size parameter of the ultrasound breast image,

computing a new surface model corresponding to the estimated value of the size parameter from the parameterized surface models, the new surface model comprising a set of computed boundary surface LUTs corresponding to the estimated value of the size parameter, and

computing estimated locations of boundary surfaces from the set of computed boundary surface LUTs of the new surface model to identify pixels of a primary layer in the ultrasound breast image.

23. The method of claim 22, further comprising:

finding representative intensity values of a plurality of control point tissues, the representative intensity values of the plurality of control point tissues including a representative intensive value of grey pixels values of subcutaneous fat layer, and

deriving a normalized image by normalizing the ultrasound breast image with respect to the plurality of control point tissues based on a mapping relationship between the representative intensity values of the plurality of control point tissues and normalized values of the plurality of control point tissues.

24. The method of claim 23, further comprising rendering the normalized image for visualization.

25. The method of claim 22, wherein the layered model includes skin layer and retro-mammary layer, the method further comprising:

estimating locations of a first boundary surface demarcating the skin layer and a second boundary surface demarcating the retro-mammary layer, and

rendering a portion of the ultrasound breast image for visualization, the portion of the ultrasound breast image excluding the skin layer and the retro-mammary layer.

26. The method of claim 22, wherein the primary layer is a mammary zone, and the method further comprising:

for each pixel in the mammary zone, estimating a probability value of the each pixel being malignant,

grouping pixels with probability values above a threshold value into contiguous regions as suspect lesions.

27. A method of identifying lesions in an ultrasound breast image, comprising the steps of:

computing estimated locations of surfaces separating primary layer tissues, said primary layer tissues including tissues in a mammary zone;

identifying pixels in the mammary zone;

constructing a pixel characteristic vector (PCV) for each pixel in the mammary zone, said PCV including at least characteristics of a neighborhood of said each pixel,

for each of the pixels in the mammary zone, computing a malignancy probability value from the PCV of the each pixel, assigning to each of the pixels the malignancy probability value and identifying a pixel as a possible lesion pixel if its assigned malignancy probability value is above a threshold value, and

reporting contiguous regions of all possible lesion pixels as potential lesions.

28. The method of claim 27, further comprising:

computing representative intensity values of a plurality of control point tissues, the control point tissues including subcutaneous fat,

normalizing grey pixel values of the pixels in the mammary zone according to a mapping relationship between assigned intensity values and the respective representative intensity values of the plurality of control point tissues, wherein the PCVs for the pixels are constructed from the grey pixel values of the pixels in the mammary zone.

29. The method of claim 27, wherein the PCV for the each pixel includes pixel specific characteristics determined solely from the each pixel.

30. The method of claim 27, further including:

calibrating a classifier on a collection of manually marked breast images, each of the manually marked breast images containing marked lesion pixels, each of the marked lesion pixels having a PCV, output of the classifier including a lesion tissue class,

wherein the step of computing the malignancy probability value from the PCV includes applying the classifier to the PCV to obtain the malignancy probability value.