Short circuits in printed circuit boards (PCBs) are among the most common failures in the manufacturing and assembly of electronic devices. The accurate detection of these defects is essential for correcting circuit malfunctions and reducing production costs. To achieve this, advanced instrumentation is employed, utilizing high-tech cameras specialized in 3D infrared thermal analysis, such as the Super IR Cam 2S Pro, designed to identify and assess temperature distributions. This study proposes a method to locate specific areas affected by short circuits in PCBs using images generated with the Super IR Cam 2S Pro and processed through morphological operations. Unlike traditional approaches that rely on binary images, this method operates on grayscale images, enhancing the detection and segmentation of affected regions. The procedure begins with image refinement through gamma correction and Laplacian filtering. Then, the image is converted to grayscale and subjected to morphological dilation. Subsequently, a morphological opening is applied to the dilated image, followed by the removal of out-of-range elements and irrelevant edges. Finally, a smoothing process using erosion is performed, yielding the remaining region corresponding to the short circuit along with the affected area. This study validates the effectiveness of the proposed method, demonstrating its applicability in detecting short circuits in PCBs through morphological processing. Furthermore, it establishes a solid foundation for future improvements, enabling its implementation in more complex scenarios involving multiple faults.