A method of semi- automated ident- ification of biomaterials, now under development, is based on the use of a combination of neural-network and of self- organizing, molecular, and supervised-learning algorithms to analyze multispectral images. The method is especially applicable to such biomaterials as tissues and cells depicted in multispectral images of biopsy specimens. A basic premise of this method is that tissues and cells that are or may be malignant can be distinguished from healthy cells by morphological and molecular differences that manifest themselves as observable differences in multispectral images.

Images of a Specimen are acquired in multiple spectral bands, then digitized, then processed by neural-network and other algorithms to identify portions that merit scrutiny by a human expert.
Heretofore, it has been necessary for a pathologist to look at all the cells in a biopsy specimen under a microscope in order to determine the degree of malignancy. This exam- ination process is fatiguing, and most cells in a typical specimen are not malignant; as a result, the process is time-consuming and usually results in many errors. An automation method like this one could reduce examination time and fatigue while increasing the accuracy of diagnosis by quickly analyzing an entire specimen and identifying those portions of the specimen to which the pathologist’s attention and expertise could be applied most productively.

The figure depicts the major steps of the analysis process according to this method. A specimen is first placed on a microscope slide. Under an epifluor- escence microscope, the specimen is exposed to light in a wavelength band selected by means of a liquid-crystal tunable filter. At present, transmission images of a specimen in approximately fifty 5-nm-wide wavelength bands that span the range from 470 to 710 nm. (In future versions, the wavelength range may be divided into a greater number of narrower bands.) The image in each wavelength band is detected and digitized (at present, to 12 bits for each pixel) by a monochrome digitizing electronic camera. The resulting multispectral image data can be represented as a stack of the images in the various wavelength bands: in the art of multispectral imaging, such a stack, or the data that it represents, is denoted an image cube.

For the purpose of identifying portions of an image that merit a pathologist’s attention to determine the degree of malignancy, an image cube is processed by a number of algorithms, some of which implement artificial neural networks:

  • The image cube is mathematically filtered to extract salient features, including sizes, shapes, and orientations of cell nuclei.
  • The unsupervised-self-organizing attribute of neural networks is exploited to partition the image cube into classes based on similarities among spectral bands in the pixels.
  • A combination of the self-organizing and supervised-learning attributes of neural networks is exploited to enable the use of a complex database of image cubes of known benign and malignant cells as a guide to further classification of unknown cells.

Development efforts have included the creation of a database of image cubes of known benign and cancerous human prostate cells. In tests thus far, the method as applied to image cubes of unknown prostate cells yielded correct classification in 98 percent of the cases.

This work was done by Hamid S. Kohen of Caltech for NASA’s Jet Propulsion Laboratory.

Topics:
Biomaterials Data acquisition and handling Diseases Fatigue Imaging and visualization Information Technology Materials properties Mathematical models Medical, health and wellness Medical, health, and wellness Neural networks Optics Simulation and modeling
This Brief includes a Technical Support Package (TSP).
Document cover
Analyzing Multispectral Signatures and Images of Biomaterials Through Neural Computing

(reference NPO-30265) is currently available for download from the TSP library.

Don't have an account?

Magazine cover
NASA Tech Briefs Magazine

This article first appeared in the September, 2002 issue of NASA Tech Briefs Magazine (Vol. 26 No. 9).

Read more articles from the archives here.

Overview

The document outlines a novel approach to analyzing biomaterials, particularly in the context of pathology, through the use of multispectral imaging and neural computing techniques. Developed under NASA's sponsorship, this method addresses the challenges faced by pathologists who traditionally examine biopsy specimens under a microscope to determine malignancy. This conventional process is time-consuming, exhausting, and prone to human error due to the sheer volume of cells that need to be analyzed.

The proposed solution, referred to as the Bio-Analysis system, utilizes multispectral imagery to create what are termed "BioCubes." These BioCubes consist of images captured at various wavelengths, specifically ranging from 470 to 710 nanometers, which represent the unique spectral distribution of different bio-materials. Each bio-material's spectral signature acts like a "fingerprint," allowing for more accurate classification and identification of pathological cells.

The system employs a sophisticated setup that includes an epifluorescence microscope equipped with a liquid-crystal tunable filter and a high-precision 12-bit camera. This technology enables the capture of detailed multispectral images, which are then processed using a hybrid neural network learning algorithm. The integration of neural computing allows for the rapid analysis of entire specimens, identifying areas that require further attention from pathologists, thereby enhancing diagnostic efficiency and accuracy.

The document also discusses the potential for future advancements in this technology, including the expansion of data channels in bio-sensors, which could lead to even more detailed analyses. The research highlights the importance of this method not only in healthcare but also in space exploration, where similar analytical techniques could be applied to biological materials.

In summary, the Bio-Analysis system represents a significant advancement in the field of pathology, offering a more efficient and accurate means of diagnosing malignancies through the innovative use of multispectral imaging and neural computing. This approach not only alleviates the burden on pathologists but also holds promise for improving patient outcomes through timely and precise diagnoses.