JESS: Java extensible snakes system
Bibliographic record
Abstract
Snakes (Active Contour Models) are powerful model-based image segmentation tools. Although researchers have proven them especially useful in medical image analysis over the past decade, Snakes have remained primarily in the academic world and they have not become widely used in clinical practice or widely available in commercial packages. A number of confusing and specialized variants exist and there has been no standard open-source implementation available. To address this problem, we present a Java Extensible Snakes System (JESS) that is general, portable, and extensible. The system uses Java Swing classes to allow for the rapid development of custom graphical user interfaces (GUI's). It also incorporates the Java Advanced Imaging (JAI) class library, which provide custom image preprocessing, image display and general image I/O. The Snakes algorithm itself is written in a hierarchical fashion, consisting of a general Snake class and several subclasses that span the main variants of Snakes including a new, powerful, robust subdivision-curve Snake. These subclasses can be easily and quickly extended and customized for any specific segmentation and analysis task. We demonstrate the utility of these classes for segmenting various anatomical structures from 2D medical images. We also demonstrate the effectiveness of JESS by using it to rapidly build a prototype semi-automatic sperm analysis system. The JESS software will be made publicly available in early 2005.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".