Diabetic foot complications, often result in ulcers due to peripheral neuropathy, peripheral arterial disease (PAD), and foot deformities. Peripheral neuropathy, prevalent in nearly half of diabetic patients, leads to sensory and motor deficits, increasing injury and ulcer risks. Motor neuropathy causes foot deformities, further elevating pressure points and ulcer risks. Infections involving gram-positive and gram-negative bacteria can progress to severe conditions like osteomyelitis and sepsis, requiring a multidisciplinary approach for management, including surgical debridement, antibiotics, and revascularization. Infrared thermography aids early detection of diabetic foot issues by identifying temperature variations indicative of underlying pathologies. This study proposes the Reverse Circular Logarithmic Local Binary Pattern (RCL-LBP), an extension of LBP, enhancing texture classification through rotation invariance and logarithmic normalization. RCL-LBP compares favorably in accuracy and sensitivity to existing LBP variants but shows moderate specificity. Evaluation was performed on nine LBP variants using XGBoost model for final classification. Experiments on thermographic images from diabetic and healthy patients reveal that RCL-LBP achieves an accuracy of 0.765 in combined LBP scenarios and 0.794 in angiosome combined scenarios, with high sensitivity (0.920) but moderate specificity (0.333 and 0.444) with average memory consumption close to 60 megabytes.