Kluwer - Handbook of Biomedical Image Analysis Vol

7.7 Conclusion and Perspectives

In this chapter, we have seen different ways of computer assistance to the diagnosis of diabetic retinopathy, which is a very frequent and severe eye-disease: image enhancement, mass screening, and monitoring. Different algorithms within this framework have been presented and evaluated with encouraging results.

However, there are still improvements to be made. The first one is to use high-resolution images. We worked on images already used in centers of ophthalmology, but it is clear that acquisition techniques also improve and that in the coming years high-resolution images will become clinical standard. Future segmentation algorithm can make use of this high resolution (e.g. there will be more features for microaneurysm detection).

Another possible research axis is the inclusion of patient data into the algorithms. This a priori knowledge about the patient is used by physicians; it also could be used by automatic methods. For instance, we have observed, that the color of black people’s eyes is quite different from the color of white people’s, the color of a child’s retina is different from the color of an adult’s eye. This is precious information that could be used in order to enhance the performance of lesions detection algorithms.

Even if there is still progress to be made, the presented algorithms work well; a clinical trial is envisaged.

7.8 Annex: Algorithm Evaluation

Whenever objects are detected automatically, the performance of the algorithm has to be evaluated. In the medical domain, results are normally compared to the results obtained by one or more specialists.

Let us consider a medical examination (diagnostic test). Often, such a test can only be positive or negative (the patient suffers from the disease or not). In order to evaluate the efficiency of this diagnostic test, its result is compared to reality; “the truth” is found by other diagnostic methods. For this comparison, we define:

True Positive (TP): The patient suffers from the disease and the test was positive.

False Positive (FP): The patient does not suffer from the disease, but the test was positive.

True Negative (TN): The patient does not suffer from the disease, and the test was negative.

False Negative (FN): The patient suffers from the disease, but the test was negative.

With these definitions, we can evaluate the performance of a diagnostic test by means of sensitivity and specificity, defined as

TP + FN is the number of patients suffering from the disease and TN + FP is the number of patients not suffering from the disease; the sensitivity is the percentage of detected cases of the disease and the specificity is the percentage of correctly classified healthy persons.

These definitions can be transfered to the evaluation of detection/classification algorithms, i.e. true positives are correctly detected pathologies, false positives are nonpathological objects falsely classified by the algorithm, etc.

There is, however, a difference between detection and classification algorithms: in detection problems, the number of objects is not limited as it is the case for classification problems (e.g. the classification of patients). In detection problems, a definition of true negatives does not make sense. There are two possibilities to resolve this problem:

If the number of objects is an important quantity (number of lesions, e.g. microaneurysms), then the number of false positives may be a good indicator for the quality of the algorithm.

If the number cannot be determined or if it is not the important quantity (this is the case if these are strong variations in shape and size of lesions— like for exudates for example), a pixel-wise comparison between the two results is preferable. In this case, the predictive value can be calculated:

This is the probability that an object (or pixel) classified as positive is really positive.

With these values (sensitivity, number of false positives, predictive value), the quality of automatic pathology detection algorithms can be assessed.

7.9 Acknowledgment

First of all, the authors thank the ophthalmology department of the Lariboisire Hospital in Paris for their excellent collaboration, their hearty and competent support, for having supplied all images, and for having evaluated the performance of all algorithms presented in this chapter.

This work has been supported by the French Ministry of Education and Research (MENRT) in the program Dpistage automatique de la retinopathie´ diabtique (00 B 0100 01).

Questions

1.Why do usual shade correction algorithms darken pixels close to bright objects?

2.How can this darkening effect be prevented?

3.What is the difference between an algebraic and a morphological closing?

4.How can dark details in a gray scale image be extracted using mathematical morphology?

5.How can dark details with a maximal extension of λ be extracted?

6.How does the use of markers in the watershed transformation work and what is their influence on the result?

7.How can the watershed transformation be used for the detection of thin dark lines in a gray scale image?

8.How can the watershed transformation be used for the detection of object contours?

9.Which morphological operator can be used for removing bright objects preserving the borders of all remaining objects?

10.In the analysis of fundus images, specificity cannot be used for an assessment of the quality of a pathology detection algorithm. Why?

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The second direction is trying to identify the tissue type distribution in plaque which is the only way to distinguish vulnerable plaques from stable plaques of similar size.

The motivation to study the constituents within carotid vessel wall is that evidence suggests different plaque tissue types yield different vulnerabilities to plaque rupture. Also, the location of plaque tissues, such as the distance to lumen, may play a role in plaque rupture. Thus, imaging and analysis techniques that are sensitive to plaque tissue types are needed and can subdivide a plaque into its constituent components. This chapter presents the postprocessing techniques developed for the identification of plaque constituents. In our laboratory, these techniques have been used to study the characteristics of the human carotid lesions that caused neurological symptoms [7] and of high cholesterolemia patients. Technically, magnetic resonance (MR) images obtained from advanced lesions in human carotid arteries present unique challenges:

1.Small size of artery wall: The carotid artery is usually less than 1 cm in diameter. Even if high-resolution imaging methods are used, practical limitations of MR scanners require the field-of-view of the image to be at least 13 by 13 cm, with a resolution of at most 512 by 512 pixels. Within these image dimensions, the subject, carotid artery, is normally about 40 by 40 to 100 by 100 pixels ranges. The comparatively small number of pixels makes the processing and analysis very challenging.

2.Complexity of tissue constituents: In our study, over 10 types of plaque tissues are identifiable within carotid artery wall, including lipid, hemorrhage, calcification, and fibrous tissue among the most clinically important. The plaque constituents may or may not be present, are generally unpredictable in terms of location, and can be intermixed.

3.Difficulties in tissue separation: Many studies have shown that any individual MR image can only distinguish between a limited numbers of plaque tissues, regardless of contrast weighting [8]. Therefore, a need exists to integrate the information obtained from several different contrast weightings, like T1W, T2W, PDW, and TOF so as to provide a single representation of all plaque constituents. To achieve this, multiple spectrum data segmentation is very critical in this study.

4.Special processing requirement on fibrous cap: The fibrous cap is a thin tissue layer that separates the lumen and other plaque tissues within the

blood vessel wall. Therefore, it is the critical feature in predicting the occurrence of rupture and monitoring the stability of patients’ diseases. As a result, specialized segmentation techniques aimed specifically at characterizing the fibrous cap must be considered.

From image processing point of view, segmentation, the process of grouping image pixels into a collection of subregions or partitions that are statistically homogeneous with respect to one or more characteristics, such as intensity, color, texture, etc., has been a very important region analysis technique in medical image applications. The eventual goal of segmentation is to aggregate those neighboring pixels with similar features as a region and separate it from the others or the background in the image.

Since the partitioned regions sometimes do not contain any semantic meaning corresponding to the real physical object in image, image segmentation technique often serves as a low-level processing step in imageprocessing procedures. However, it is very crucial to the success of higherlevel recognition process and plays as a deterministic role to the eventual performance.

There are three categories of MR data to be analyzed in this study: single contrast weighting gray level images, image sequences, and multiple contrast weighting images. Different from what are often analyzed in other applications, the images in this study are of lower quality due to the various noises involved in the imaging process. In addition, they are with more complicated structure than subjects are usually analyzed in other medical image studies, such as brain.

From the segmentation technique point of view, numerous approaches have been developed in the last decade, which are summarized in literature reviews [1, 9, 10]. These methods are implemented from different perspectives and have shown their successes by applying to various images. They can roughly be classified as following categories: region growing and splitting [11–14], edge detecting [15–18], random filed modeling [19–22], active contour modeling [23–27], as well as some hybrid of these methods [28–31].

After careful analysis of those existing approaches, however, it is easy to see that most of the algorithms presented in the literature can work well only for certain particular types of image and their performances are good if some image formation processes are taken into account in the segmentation procedure. Because of this intrinsic applicability limitation in the proposed models, it is