Traditional autofocus methods were designed for microscopes driven by single processor computers. As computers are developed that exploit massive parallelism when acquiring and analyzing images, parallel cellular logic techniques became available to focus automatically. This paper introduces the reader to both cellular logic techniques for autofocus and a new spectral moment autofocus measure. It then compares these methods with more traditional autofocus methods. It is shown that traditional methods based on measurements of image power-give the best results when tested on one set of real images and two sets of synthetic images. The next best methods are the cellular logic and spectral moment techniques, while the worst are those based on the image probability density function or histogram.
A6btmct-Wular logic opentions (CLO's) are performed dieitpny to ~~a n~r r n y d & t P P (~,~) i n t o a n e w & t n~m y~( I , I ) . The value of each dement m the new array is determined by its value in the o r @ d array and the original values of its nearest neighbors. The neighborhad configuration (tesseUation) is usually Cpned the "cell"; whence the tern "cellular logic." CLO's may be categodzed according to the twseIlntion in which they are embedded and according to the type or typea of CLO sequences sequences which are carried out in a single step; those wMch iterate the same CLO for many steps; those which repetitively alternate subsequences of CLO strings. The effect of the CLO sequence on tbe contents of the data amy is frequently one of boundary modification. Depending on the CLO sequence(s) utilized, a boundary may be expanded to form the convex hull, or reduced so as to form the convex kernel, skeleton, or d u e . As of 1977, cenulnr logic computers have become a commercial product in biomedical image ptocessing where they are used m clinicrl i n s t r u m e n t s w f i o s e p~b t o~w h i t e M o o d c e l l i m r g e s a t r a t~ of d thousand per hour. Many other applicrtioRs are foreseen and, as further examples, preliminary results m automatic X-ray image orulysis and tissue image analysis am presented.
Dynamic images are temporal sequences of images, where the intensities of certain regions of interest (ROI's) change with time, whereas anatomical structures remain stationary. Here, new applications of dynamic image analysis, called similarity mapping, are reviewed. Similarity mapping identifies regions in a dynamic image sequence according to their temporal similarity or dissimilarity with respect to a reference ROI. Pixels in the resulting similarity map whose temporal sequence is similar to the reference ROI have high correlation values and are bright, while those with low correlation values are dark. Therefore, similarity mapping segments structures in a dynamic image sequence based on their temporal responses rather than spatial properties. The authors describe the abilities of similarity mapping to identify different image structures present in several dynamic MRI datasets with potential clinical value. They demonstrate that similarity mapping technique has been successful in identifying the following structures: 1) renal cortex and medulla, 2) activated areas of the brain during photic stimulation, 3) ischemia in the left coronary artery territory, 4) lung tumor, 5) tentorial meningioma, and 6) a region of focal ischemia in brain.
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