The　nonballasted　rail　tracks　have　been　extensively　applied　in　the　new　high-speed　railway　system　development　in　China,　that　is,　the　China　Railway　Track　System　(CRTS).　However,　many　defects　have　been　identified　during　the　operation　of　the　CRTS,　among　which　concrete　slab　crack　is　recognized　as　one　of　the　most　common,　yet　critical　defects　that　require　accurate　identification　and　timely　maintenance　attention.　Because　of　the　unique　outlook　of　the　cracks　in　nonballasted　rail　track　slabs　captured　in　the　survey　imagery,　direct　adaptations　of　the　existing　crack　extraction　methods　show　dramatically　degraded　performance.　A　new　automatic　crack　identification　method　is　developed　in　this　study　by　employing　a　region-based　active　contour　framework　with　the　intensity　cluster　energy.　The　proposed　method　embodies　three　major　contributions,　including　(1)　a　heavy　penalization　energy　component　that　could　effectively　avoid　both　under-　and　overevolutions;　(2)　a　multiphase　level-set　function　that　effectively　evolves　the　contours　generated　with　different　intensity　clusters;　and　(3)　a　two-step　implementation　of　the　framework　that　significantly　improves　the　efficiency.　The　experimental　test　evaluates　the　performance　of　the　proposed　method　using　the　data　collected　on　high-speed　rail　tracks　in　Hebei　Province,　China.　The　proposed　method　accurately　identifies　more　than　93.0%　of　digitized　cracks　in　different　crack　patterns　and　challenging　backgrounds　using　a　data　set　consisting　of　1,500　synthetic　images　and　150　actual　images.　In　addition,　it　shows　promising　performance　in　comparison　with　other　popular　state-of-the-art　crack　detection　algorithms　in　terms　of　accuracy　and　computational　efficiency.　The　proposed　method　has　demonstrated　the　promising　capacity　to　support　a　reliable　and　efficient　nonballasted　rail　track　crack　identification　and　to　facilitate　the　subsequent　maintenance　cost-effectively.