Visual criteria for diagnosing diffused liver diseases from ultrasound images can be assisted by computerized tissue classification. Feature extraction algorithms are proposed in this paper to extract the tissue characterization parameters from liver images. The resulting parameter set is further processed to obtain the minimum number of parameters which represent the most discriminating pattern space for classification. This preprocessing step has been applied to over 120 distinct pathology-investigated cases to obtain the learning data for classification. The extracted features are divided into independent training and test sets, and are used to develop and compare both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum classification error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms of classification based on statistical and neural network methods are presented and tested. The authors show that very good diagnostic rates can be obtained using unconventional classifiers trained on actual patient data.

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Purpose To evaluate the position and shape of the originally-defined clinical target volume (CTV) over the treatment course, and assess the impact of gross tumor volume (GTV)-based online CT guidance on CTV localization accuracy. Methods and Materials Weekly breath hold CT scans were acquired in 17 patients undergoing radiotherapy. Deformable registration was used to propagate the GTV and CTV from the first weekly CT to all other weekly CT images. The on-treatment CT scans were registered rigidly to the planning CT scan based on the GTV location to simulate online guidance, and residual error in the CTV centroids and borders was calculated. Results The mean GTV volume after five weeks relative to volume at the beginning of treatment was 77% +/− 20%, while for the prescribed CTV it was 92% +/− 10%. The mean absolute residual error magnitude in the CTV centroid position after a GTV-based localization was 2.9 mm +/− 3.0 mm, and varied from 0.3 mm to 20.0 mm over all patients. Residual error of the CTV centroid was associated with GTV volume regression and anisotropy of regression during treatment (p=0.02 and 0.03, Spearman rank correlation). 77% of patients and 50% of fractions had a residual error in CTV border position greater than 2 mm. Of these fractions, residual error of the CTV borders was 3.5 +/− 1.6 mm (LR), 3.1 +/− 0.9 mm (AP), and 6.4 +/− 7.5 mm (SI). Conclusions Online guidance based on the visible GTV produces substantial error in CTV localization, particularly for highly-regressing tumors. The results of this study will be useful in designing margins for CTV localization or for developing new online CTV localization strategies.

Purpose: To optimize modeling of interfractional anatomical variation during active breath-hold radiotherapy in lung cancer using principal component analysis ͑PCA͒. Methods: In 12 patients analyzed, weekly CT sessions consisting of three repeat intrafraction scans were acquired with active breathing control at the end of normal inspiration. The gross tumor volume ͑GTV͒ and lungs were delineated and reviewed on the first week image by physicians and propagated to all other images using deformable image registration. PCA was used to model the target and lung variability during treatment. Four PCA models were generated for each specific patient: ͑1͒ Individual models for the GTV and each lung from one image per week ͑week to week, W2W͒; ͑2͒ a W2W composite model of all structures; ͑3͒ individual models using all images ͑weekly plus repeat intrafraction images, allscans͒; and ͑4͒ composite model with all images. Models were reconstructed retrospectively ͑using all available images acquired͒ and prospectively ͑using only data acquired up to a time point during treatment͒. Dominant modes representing at least 95% of the total variability were used to reconstruct the observed anatomy. Residual reconstruction error between the model-reconstructed and observed anatomy was calculated to compare the accuracy of the models. Results: An average of 3.4 and 4.9 modes was required for the allscans models, for the GTV and composite models, respectively. The W2W model required one less mode in 40% of the patients. For the retrospective composite W2W model, the average reconstruction error was 0.7Ϯ 0.2 mm, which increased to 1.1Ϯ 0.5 mm when the allscans model was used. Individual and composite models did not have significantly different errors ͑p = 0.15, paired t-test͒. The average reconstruction error for the prospective models of the GTV stabilized after four measurements at 1.2Ϯ 0.5 mm and for the composite model after five measurements at 0.8Ϯ 0.4 mm. Conclusions: Retrospective PCA models were capable of reconstructing original GTV and lung shapes and positions within several millimeters with three to four dominant modes, on average. Prospective models achieved similar accuracy after four to five measurements.

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Quantitative measurements of tumor volume becomes more realistic with the use of imaging- particularly specially when the tumor have non-ellipsoidal morphology, which remains subtle, irregular and difficult to assess by visual metric and clinical examination. The quantitative measurements depend strongly on the accuracy of the segmentation technique. The validity of brain tumor segmentation methods is an important issue in medical imaging because it has a direct impact on many applications such as surgical planning and quantitative measurements of tumor volume. Our goal was to examine two popular segmentation techniques seeded region growing and active contour "snakes" to be compared against experts' manual segmentations as the gold standard. We illustrated these methods on brain tumor volume cases using MR imaging modality.